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The United Kingdom Lung Cancer Screening Trial: Report on a pilot Randomised Controlled Trial of Low Dose Computed Tomography screening for the early detection of lung cancerAuthorsField JK1, Duffy SW2, Baldwin DR3, Brain KE4, Devaraj A5, Eisen T6, Green BA1, Holemans JA7, Kavanagh T8, Kerr KM9, Ledson M7, Lifford KJ4, McRonald FE1, Nair A10, Page RD7, Parmar MKB11, Rintoul RC12, Screaton N12, Wald NJ2, Weller D13, Whynes DK14, Williamson PR1, Yadegarfar G1, Hansell DM15Corresponding Author: Professor John K. Field: j.k.field@liv.ac.ukAffiliations1University of Liverpool, Liverpool, UK; 2Queen Mary University of London, London, UK; 3Department of Respiratory Medicine, Nottingham University Hospitals, Nottingham, UK; 4Cardiff University School of Medicine, Cardiff, UK; 5St George's Hospital, London, UK; 6University of Cambridge, Cambridge, UK; 7Liverpool Heart & Chest Hospital, Liverpool UK; 8Lung Cancer Patient Advocate, Liverpool, UK; 9Aberdeen Royal Infirmary, Aberdeen, UK; 10Guy's and St Thomas' NHS Foundation Trust, London, UK; 11Medical Research Council, London, UK; 12Papworth Hospital NHS Foundation Trust, Cambridge, UK; 13University of Edinburgh, Edinburgh, UK; 14University of Nottingham, Nottingham, UK; 15Royal Brompton and Harefield NHS Foundation Trust, London, UKCompeting Interests: Dr Eisen declared: share ownership and corporate sponsored research from AstraZeneca. He is on the advisory boards of AVEO, Astellas, BMS, Bayer, Boehringer Ingelheim, GSK, Novartis, Pfizer and Roche. He has declared corporate sponsored research from Bayer, GSK, Pfizer. He has received honoraria from AVEO, Astellas, BMS, Bayer,BI, GSK, Novartis, Pfizer and Roche.REPORT WORD COUNT (main body) : 40,043ABSTRACTBackgroundLung cancer kills more people than any other cancer in the UK, (five-year survival < 13%). Early diagnosis can save lives. The USA-based National Lung Cancer Screening Trial (NLST) reported a 20% relative-reduction in lung cancer mortality and 6.7% all-cause mortality in LDCT-screened subjects.ObjectivesAnalyse LDCT lung cancer screening in a high risk UK population, determine optimum recruitment, screening, reading and care-pathway strategies; assess the psychological consequences and the health economic implications of screening.DesignA pilot randomised-controlled trial comparing intervention with usual care. A population-based risk questionnaire identified individuals at high-risk of developing lung cancer (>5% over five years).SettingThoracic centres with expertise in lung cancer imaging, respiratory medicine, pathology and surgery: Liverpool Heart & Chest Hospital, Merseyside and Papworth Hospital, Cambridgeshire.ParticipantsIndividuals 50-75 years, at high-risk of lung cancer in the PCTs adjacent to the centres.InterventionsA thoracic LDCT scan. Follow-up CTs as per protocol. Referral to MDT clinics determined by nodule size criteria.Main outcome measuresPopulation-based recruitment based on risk-stratificationManagement of trial through web-based databaseOptimal characteristics of CT readers (radiologists vs radiographers)Characterisation of CT-detected nodules utilising volumetric analysisPrevalence of lung cancer at baselineSocio-demographic factors affecting participationPsychosocial measures (cancer distress, anxiety, depression, decision satisfaction)Cost-effectiveness modellingResults247,354 population approaches. 30.7% responded positively to the screening invitation. Recruitment: 2,028 - CT arm, 2,027 - control arm.1994 participants underwent CT scan. 42 participants (2.1%) were diagnosed with lung cancer. 36/42, (85.7%) of the screen-detected cancers were identified as stage 1 or 2 and 35 (83.3%) underwent surgical resection as their primary treatment. Lung cancer was more common in the lowest socioeconomic group.Short-term adverse psychosocial consequences were observed in participants randomised to the intervention arm, and in those with a major lung abnormality detected, but these differences were modest and temporary. Roll-out of screening as a service or design of a full trial would need to address issues of outreach. Health economic analysis suggests that the intervention could be cost-effective, but this needs to be confirmed using data on actual lung cancer mortality.ConclusionsThe UKLS pilot trial was successfully undertaken with 4,055 randomised individuals. The data from the UKLS trial provides evidence that adds to existing data to suggest that lung cancer screening in the UK could potentially be implemented in the 60-75 year age group, selected via the LLPv2 risk-model and using CT volumetry-based management protocols. Future WorkThe UKLS data will be pooled with the NELSON and other EU trials in 2016, which will provide European mortality and cost effectiveness data. For now, there is a clear need for mortality results from other trials, and for further research to identify optimal methods of implementation and delivery. Strategies for increasing uptake and providing support for under-served groups will be key to implementation.Trial registrationISRCTN 78513845FundingFunded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment. See HTA programme website for further information.Word Count: 499TABLE OF CONTENTS TOC \o "1-2" \u TABLE OF CONTENTS PAGEREF _Toc436653841 \h 5LIST OF TABLES AND FIGURES PAGEREF _Toc436653842 \h 8Abbreviations PAGEREF _Toc436653843 \h 12SCIENTIFIC SUMMARY PAGEREF _Toc436653844 \h 14Background PAGEREF _Toc436653845 \h 14Objectives PAGEREF _Toc436653846 \h 15Methods PAGEREF _Toc436653847 \h 15Results PAGEREF _Toc436653848 \h 16PLAIN ENGLISH SUMMARY PAGEREF _Toc436653849 \h 21CHAPTER 1: INTRODUCTION PAGEREF _Toc436653850 \h 221.1Lung cancer: the clinical problem PAGEREF _Toc436653851 \h 221.2Risk factors for lung cancer PAGEREF _Toc436653852 \h 231.3Risk models for lung cancer prediction PAGEREF _Toc436653853 \h 241.4Screening for lung cancer PAGEREF _Toc436653854 \h 241.5Lung Cancer Screening in Clinical Practice in the United Kingdom PAGEREF _Toc436653855 \h 291.6The United Kingdom Lung Cancer Screening Trial PAGEREF _Toc436653856 \h 30CHAPTER 2: METHODS PAGEREF _Toc436653857 \h 332.1Objectives PAGEREF _Toc436653858 \h 332.2Trial design PAGEREF _Toc436653859 \h 332.3Participants PAGEREF _Toc436653860 \h 352.4Randomisation and blinding PAGEREF _Toc436653861 \h 382.5Interventions PAGEREF _Toc436653862 \h 392.6UKLS trial working with primary care PAGEREF _Toc436653863 \h 392.7Outcomes PAGEREF _Toc436653864 \h 412.8Sample size PAGEREF _Toc436653865 \h 42CHAPTER 3: RECRUITMENT OF PARTICIPANTS PAGEREF _Toc436653866 \h 463.1Introduction and rationale PAGEREF _Toc436653867 \h 463.2Methods PAGEREF _Toc436653868 \h 463.3Results PAGEREF _Toc436653869 \h 463.4Discussion PAGEREF _Toc436653870 \h 573.5Conclusion PAGEREF _Toc436653871 \h 61CHAPTER 4: RADIOLOGICAL INTERVENTIONS AND OUTCOMES PAGEREF _Toc436653872 \h 624.1Introduction PAGEREF _Toc436653873 \h 624.2Methods PAGEREF _Toc436653874 \h 624.3Results PAGEREF _Toc436653875 \h 724.4Discussion PAGEREF _Toc436653876 \h 77CHAPTER 5: CT scan findings and the early outcome of patients referred to the multidisciplinary team PAGEREF _Toc436653877 \h 815.1Introduction PAGEREF _Toc436653878 \h 815.2Methods PAGEREF _Toc436653879 \h 815.3Results PAGEREF _Toc436653880 \h 835.4Discussion PAGEREF _Toc436653881 \h 98CHAPTER 6: PSYCHOSOCIAL OUTCOMES PAGEREF _Toc436653882 \h 1006.1Background PAGEREF _Toc436653883 \h 1006.2Aim PAGEREF _Toc436653884 \h 1026.3Objectives PAGEREF _Toc436653885 \h 1026.4Hypotheses PAGEREF _Toc436653886 \h 1026.5Methods PAGEREF _Toc436653887 \h 1026.6Results PAGEREF _Toc436653888 \h 1086.7 Discussion PAGEREF _Toc436653889 \h 1406.8 Summary and recommendations PAGEREF _Toc436653890 \h 142CHAPTER 7: Health Economics PAGEREF _Toc436653891 \h 1447.1Introduction PAGEREF _Toc436653892 \h 1447.2Current costs PAGEREF _Toc436653893 \h 1467.3Future benefits PAGEREF _Toc436653894 \h 1497.4Future costs avoided PAGEREF _Toc436653895 \h 1537.5Cost-effectiveness PAGEREF _Toc436653896 \h 1547.6Summary PAGEREF _Toc436653897 \h 158CHAPTER 8: CONCLUSION PAGEREF _Toc436653898 \h 1638.1Overview of the main findings from the UKLS trial PAGEREF _Toc436653899 \h 1638.2Implications of UKLS trial for lung cancer screening implementation PAGEREF _Toc436653900 \h 1648.3Acceptability of screening PAGEREF _Toc436653901 \h 1688.4Lung cancer screening and integrated smoking cessation PAGEREF _Toc436653902 \h 1698.5Strengths and limitations of the UKLS pilot trial PAGEREF _Toc436653903 \h 1698.6Prioritised research recommendations PAGEREF _Toc436653904 \h 1708.7Future prospects PAGEREF _Toc436653905 \h 171Acknowledgements PAGEREF _Toc436653906 \h 174Contributions of authors PAGEREF _Toc436653907 \h 175Data Sharing PAGEREF _Toc436653908 \h 178Publications PAGEREF _Toc436653909 \h 178References PAGEREF _Toc436653910 \h 181Appendix 1:Details of Protocol Amendments PAGEREF _Toc436653911 \h 191Appendix 2:Details of the LLPv2 Risk Model PAGEREF _Toc436653912 \h 193Appendix 3:UKLS Trial Documentation PAGEREF _Toc436653913 \h 195Appendix 4:Trial randomisation method outlined in Section 2.4.1 PAGEREF _Toc436653914 \h 210Appendix 5:Data tables showing demographics of response and recruitment PAGEREF _Toc436653915 \h 212Appendix 6:Summary Report of UKLS sample QC PAGEREF _Toc436653916 \h 227Appendix 7:Psychosocial Tables PAGEREF _Toc436653917 \h 229LIST OF TABLES AND FIGURESTable 1: Overview of the European randomised lung cancer CT screening trials, based on the 2010 data; adapted from Field et al ADDIN EN.CITE <EndNote><Cite><Author>Field</Author><Year>2013</Year><RecNum>1049</RecNum><DisplayText>(46)</DisplayText><record><rec-number>1049</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1049</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Field, J. K.</author><author>Hansell, D. M.</author><author>Duffy, S. W.</author><author>Baldwin, D. R.</author></authors></contributors><auth-address>Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, Liverpool, UK. Electronic address: J.K.Field@liv.ac.uk.</auth-address><titles><title>CT screening for lung cancer: countdown to implementation</title><secondary-title>Lancet Oncol</secondary-title><alt-title>The lancet oncology</alt-title></titles><periodical><full-title>Lancet Oncol</full-title><abbr-1>The lancet oncology</abbr-1></periodical><alt-periodical><full-title>Lancet Oncol</full-title><abbr-1>The lancet oncology</abbr-1></alt-periodical><pages>e591-600</pages><volume>14</volume><number>13</number><dates><year>2013</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>1474-5488 (Electronic)&#xD;1470-2045 (Linking)</isbn><accession-num>24275132</accession-num><urls><related-urls><url>(13)70293-6</electronic-resource-num></record></Cite></EndNote>46. NELSON= Dutch-Belgian lung cancer screening trial. DLCST = Danish lung cancer screening trial. LUSI = German Lung Cancer Screening Intervention Study. ITALUNG = Italian Lung cancer Computed Tomography screening trial. DANTE = Detection and screening of early lung cancer by Novel imaging Technology and molecular assays. MILD = Multi-centric Italian Lung Detection Trial. Data are correct to August 2010. **Cut off date 1st January 2011.Table 2 Comparison of radiographer and radiologist sensitivity for the 10 radiographer-radiologist combinations. A negative value for the difference in sensitivity indicates a lower radiologist sensitivity compared to a radiographer. P values are those derived from the McNemar’s test. P values in bold indicate statistically significant results.Table 3 Comparison of radiographer and radiologist average FPs per case for the 10 radiographer-radiologist combinations. A negative value for the difference indicates a lower radiologist average FPs per case compared to a radiographer. P values are those derived from the paired student’s t-test. P values in bold indicate statistically significant results.Table 4 Sensitivity of radiologists for each reading method. Except for P values, figures shown are percentages. P values are those derived from the chi-square test. P values in bold indicate statistically significant results.Table 5 Average FPs per case for each reading method. A negative difference indicates a lower average FP per case with concurrent compared to independent reading. P values are those derived from the independent samples t-test. P values in bold indicate statistically significant resultsTable 6 Mean reading times of radiologists for each reading method. Numbers shown are time in minutes, except for P values. Numbers in parentheses are 95% confidence intervals for the mean. A negative difference indicates a shorter time with concurrent compared to independent reading. P values are those derived from the independent samples t-test. P values in bold indicate statistically significant results.Table 7 Sensitivities of radiologists in a selection of nodule detection studies. NR=not reported. Table 8: Demographic, risk and medical characteristics of n=4,055 individuals randomised to the UKLS intervention (CT screen) and control (non-screen) trial armsTable 9: Overall numbers of individuals in each nodule category, numbers referred to the multidisciplinary team (MDT) and number of lung cancers diagnosedTable 10: Details of cancers diagnosed Table 11 Work up by MDTTable 12: Clinically significant thoracic incidental findings, for which a supplementary radiology report was sent to the GP or appropriate MDTTable 13: Clinically significant extra-thoracic incidental findings, for which a supplementary radiology report was sent to the GP or appropriate MDTTable 14: Univariable and multivariable analyses of factors influencing trial uptake in high risk individualsTable 15: Univariable and multivariable analyses of level of affective risk perception influencing trial uptake in high risk individualsTable 16: Comparison of T1 completers and non-completers Table 17: Comparison of T2 completers and non-completers Table 18: Baseline psychosocial sample characteristics by trial allocation Table 19: T1 sample baseline characteristics by trial allocation Table 20: Short-term psychosocial outcomes of trial allocation Table 21: Short-term psychosocial outcomes of baseline screening results Table 22: Short-term decision satisfaction according to baseline screening resultTable 23: Long-term psychosocial outcomes of trial allocation Table 24: Impact of screening outcome at long-term follow-upTable 25: Long-term decision satisfaction in screening outcome groupsTable 26: Univariable analyses of the difference in logn cancer distress between trial allocation groups for low baseline distress scorers, using a linear mixed effects model Table 27: Cancer distress effect modifiers analysed over T1 and T2 using a linear mixed effects model for participants with low baseline cancer distressTable 28: Multivariable analyses for cancer distress adjusted for important effect modifiers - low baseline distressTable 29: Multivariable analyses for cancer distress adjusted for important effect modifiers - high baseline cancer distressTable 30: Unit costs of procedures, 2011-12Table 31: Assumed survival rates and corresponding parameters for survival estimationTable 32: Comparison of cost effectiveness analyses of NLST (USA) and UKLS (UK)Table 33: Comparison of T0 (touch screen) completers and non-completers Table 34: Comparison of baseline CT scan attenders and non-attendersTable 35: Comparison of baseline screening test result groups on demographic and baseline psychosocial variablesTable 36: T2 sample characteristics by trial allocation Table 37: Comparison of screening outcome groups (long-term follow-up) on demographic and baseline psychosocial variablesTable 38: Mean baseline cancer distress scores (original scale) by recruitment site and deprivation.Figure 1: UKLS Trial Recruitment and implementation processFigure 2: Showing participant flow from initial contact to CT screeningFigure 3: Impact of age upon initial response rate, LLP risk and trial consent rate Figure 4: Age distribution of the original approached population sample, positive responders, high risk responders and recruits to the RCT. In the latter two categories, percentages for the youngest age group are shown to the left of the y-axis. Nearly 95% of trial recruits were aged 61 or over.Figure 5: Percentage of UKLS positive responders (n=75,958) with an LLP risk of >5%, by individual year of age. (There are n=8 individuals aged 76y; these are included with the 75 year old group). See Appendix 5 for primary data.Figure 6: Social demographics of UKLS sample. Distribution of individuals within IMD National Quintiles, by area. Much more deprivation is seen in the Liverpool areas (Liverpool, Knowsley and Sefton), than in the Cambridgeshire areas (Cambridgeshire, Peterborough and Bedfordshire). However, the overall socioeconomic distribution of the UKLS sample is similar to that of the entire English population: this was an inherent feature of the study design.Figure 7: Impact of socioeconomic status upon initial response rate (lower line), LLP risk (bars) and trial consent rate (upper line).Figure 8: Socioeconomic distribution of the original approached population sample, positive responders, high risk responders and recruits to the RCT. The social demographic spectrum of the high risk individuals participating in the trial is roughly in proportion to that of the original approached sample.Figure 9: Positive Response rate to initial approach, stratified by both region (North/Liverpool and South/Cambridgeshire) and IMD Quintile. A slightly better response rate was observed in the Southern region in each IMD quintile.Figure 10: Smoking status amongst UKLS positive and negative responders, and likely smoking status amongst non-responders, based on expected values from age 50+ population figures less known figures from responders. In this case, ‘non-responders’ includes 2,024 positive responders and 764 negative responders who did not provide their smoking status.Figure 11: Interaction between smoking status and socioeconomic status in determining initial response to the UKLS invitation. Calculated positive response rate, percent, by smoking status and IMD Quintile. The line (and percentages) denotes the known positive response rate of all individuals within that IMD quintile. Calculations were based on Health Survey for England population smoking figures for each IMD quintile, for an age group of 50-75 years (see Appendix 5).Figure 12: UKLS Nodule Care Pathway Management Protocol Figure 13: Screenshot from UKLS database. Nodules at baseline, 3 months and 12 months CT are matched in a row. The second figure in each box refers to the slice number and the 3rd figure represents nodule volume. By hovering over the nodule, a figure for VDT is generated automatically. All nodules with a significant VDT < 400 days are highlighted in purple (not shown in this example). Figure 14: Diagnostic tree showing outcomes of 1994 baseline scansFigure 15: Trial participation flow diagramFigure 16: Screening outcome groups at T2Figure 17: Pathway for Management of indeterminate and positive findings detected by low-dose CTFigure 18: Screening Flow ChartAbbreviationsACRINAmerican College of Radiology Imaging NetworkCADComputer Aided DetectionCOPDChronic Obstructive Pulmonary DiseaseCTComputed TomographyCWS-RCancer Worry Scale (adapted for lung cancer)DLCSTDanish Lung Cancer Screening TrialELCAPEarly Lung Cancer Action ProjectFEV1Forced Expiratory Volume in 1 secondFPFalse PositiveFVCForced Vital CapacityGCLPGood Clinical Laboratory PracticeGPGeneral PractitionerHADSHospital Anxiety and Depression ScaleHESHospital Episode StatisticsHRGHealthcare Resource GroupsHRQLHealth-Related Quality of LifeHTAHealth Technology AssessmentIARCInternational Agency for Research on CancerICERIncremental Cost Effectiveness RatioIMDIndex of Multiple DeprivationKVpKilovolt PeakLDCTLow Dose Computed TomographyLECMCLiverpool Experimental Cancer Medicine CentreLHCHLiverpool Heart and Chest HospitalLLPLiverpool Lung ProjectLSOALower layer Super Output AreamAsmilliAmp secondsMDCTMulti-detector CT: 16 channel (or higher)MDTMultidisciplinary TeammGymilliGraymSvmilliSievertNCINational Cancer Institute (USA)NELSONNederlands Leuvens Longkanker Screenings Onderzoek (Dutch-Belgian Randomised Lung Cancer Screening Trial)NHSNational Health ServiceNIHRNational Institute for Health ResearchNIGBNational Information Governance BoardNLSTNational Lung Screening Trial (USA)NRESNational Research Ethics ServiceONSOffice for National StatisticsPCTPrimary Care TrustPLCOProstate, Lung, Colorectal and Ovarian Screening Trial (USA)QALYQuality Adjusted Life YearRBHRoyal Brompton and Harefield NHS Foundation Trust RCTRandomised Controlled TrialSOPStandard Operating ProcedureTBTuberculosisUKLSUnited Kingdom Lung Screening TrialUSPSTFUnited States Preventive Services Task ForceSCIENTIFIC SUMMARYBackgroundLung Cancer: the clinical problemLung cancer kills more people than any other cancer. Its estimated cost to the NHS is ?9,071 per patient per year, and the total cost to the UK economy is ?2.4 billion; more than any other cancer. The overall five-year lung cancer survival rate in the UK is <13%, largely due to late presentation. However, the five-year survival with stage 1a disease is over 70%. Early diagnosis using CT screening has the potential to save many lives, as well as reduce public puted Tomography Screening for lung cancerThe USA-based National Lung Cancer Screening Trial (NLST) recently reported a 20% relative-reduction in lung cancer mortality and 6.7% all-cause mortality in subjects randomised to Low Dose Computed Tomography (LDCT) scans versus chest X-ray. The final results of the Dutch-Belgian trial (NELSON) are due in ~2016. Successful and cost-effective screening for lung cancer is dependent upon identifying and targeting high risk individuals, utilising algorithmic risk models that take into account known risk-factors for lung cancer: tobacco use, age, previous respiratory disease, family, medical history and occupational exposures. The group selected should be of sufficiently high risk that the benefits will outweigh the likely harms.Lung Cancer Screening in the United KingdomThe National Institute for Health Research Health Technology Assessment (NIHR HTA) programme provided funding for a pilot study of Low Dose CT (LDCT) screening in the UK population to address specific questions, and inform the health service on the future implementation of lung cancer screening. The United Kingdom Lung Cancer Screening Trial (UKLS) is the subject of this report. Data from UKLS, when pooled with that from NELSON and other European trials, will potentially provide a sample of 36,000 subjects and associated mortality and cost-effectiveness figures. ObjectivesThe overall aim of the UKLS pilot trial is to contribute to the data required for an informed decision regarding the introduction of population-screening for lung cancer. This involves determining the best recruitment and screening strategies and assessing the physical and psychological consequences and health economic implications of screening. As the UKLS pilot trial is insufficiently powered to demonstrate a reduction in mortality, a further objective is to provide results for pooling with current European lung screening studies.Main outcome measuresPopulation-based recruitment based on risk-stratificationManagement of trial through web-based databaseOptimal characteristics of CT readers (radiologists vs radiographers)Characterisation of CT-detected nodules utilising volumetric analysisPrevalence of lung cancer at baselineSocio-demographic factors affecting participationPsychosocial measures (cancer distress, anxiety, depression, decision satisfaction)Cost-effectiveness modellingMethodsUKLS is a Randomised Controlled Trial (RCT) of LDCT versus usual care for the early detection of lung cancer. Primary Care Trust (PCT) records for 50-75 year olds were used to approach individuals by letter The Liverpool Lung Project (LLP) lung cancer risk prediction algorithm (LLPv2) was used to calculate the positive responders’ risk status, who were invited. The design of the trial follows the Wald Single-Screen Design.The UKLS pilot trial was undertaken in six PCTs around two specialist thoracic hospitals (Liverpool Heart and Chest Hospital (LHCH); and Papworth Hospital, Cambridgeshire, respectively). 4,055 high-risk subjects were recruited and randomised in a 1:1 ratio to receive either a single thoracic LDCT scan or usual care. Subjects were invited to a recruitment clinic, shown a DVD, given time to discuss the trial with a Research Nurse, and consented. Subjects had their lung function measured, provided blood and other samples, completed detailed epidemiological, psychosocial and health economics questionnaires and were offered smoking cessation advice. The participants were randomised within a two-week period and the CT-scan group received a CT scan appointment. The CT scans were double-read, using state-of-the-art volumetric analysis by radiologists and radiographers based at pilot sites. Second reads were undertaken at the Royal Brompton and Harefield NHS Foundation Trust, London (RBH). Any nodules identified on LDCT-scans were treated as defined in the UKLS care-pathway protocol. Health and mortality outcomes of participants in both study arms will be followed up for ten years, via the Office for National Statistics (ONS), the Hospital Episode Statistics (HES) database, and the National Cancer Registration Service.Classification and management of findingsCT findings classification was based on the UKLS radiology protocol, utilising nodule diameter and volume, utilising Siemens LungCARE software platform and classified into four categories:No nodules or Category 1 (benign) nodules: no further action requiredCategory 2 (small, probably benign) nodules: follow up CT scan at 12 monthsCategory 3 (larger, potentially malignant) nodules: follow up CT scan at 3 months and 12 monthsCategory 4 (higher chance of malignancy) nodules: immediate referral to Multidisciplinary Team (MDT).Where follow-up scans (3 or 12 months) were performed, the nodule volume doubling time (VDT) was calculated.ResultsDemographics of trial participantsThe UKLS approached 247,354 individuals in the two pilot sites, 75,958 people (30.7%) responded positively to the screening invitation. Demographic factors associated with positive response were: higher socioeconomic status, age 56-70 years, and ex-smokers. Those from lower socioeconomic groups and current smokers were less likely to respond. 8,729 (11.5%) positive responders were calculated as having a high risk of lung cancer. Those categorised at high risk were more often elderly, current smokers, of lower socioeconomic status and males (2.4x females). 4,061 (46.5%) of the high risk positive responders consented to participate, and 4,055 were randomised.RadiologyCT scans were read by radiologists and specially trained radiographers, who were audited, reading CT scans both independently and concurrently. Sensitivity and false positive (FP) rates were calculated by comparing readings against reference standards; these consisted of the consensus view of radiologists at the central reading site (RBH), including a senior radiologist with >20 years experience.The mean nodule-detection sensitivity for four radiographers was 71.6% (± 8.5%), versus 83.3% (± 8.1%) for three radiologists. The number of FPs per scan ranged from 0.6-2.9 for the radiographers and from 0.2-0.7 for the radiologists.Our results suggest that trained radiographers are currently unsuitable to act as sole readers in lung cancer screening, but that they may improve workflow for the radiologists and developed within a lung cancer screening programme.Results of screening2,028 high risk trial participants were randomised to the screening arm of UKLS, and 1994 of these received a thoracic CT scan. 979 participants had clear scans, 479 were scheduled for a 12 month follow up scan, 472 were scheduled for a three month follow up scan, and 64 were referred directly to the multidisciplinary team (MDT). The CT scans with incidental findings unrelated to lung cancer were referred to the relevant MDT or their own GP.536 subjects (i.e. 472 category 3, 64 category 4) had nodules requiring a repeat scan 41 of the Cat 4 individuals were subsequently found to have lung cancer. However, it should be noted that a repeat CT at three months for category 3 nodules was mandated by the protocol.Due to our failsafe policy reflecting the single screen design, there were a further 479 individuals for whom a repeat screen was recommended at 12 months; only one of these was found to have a confirmed cancer.At the time of analysis, 1952/1994 (97.9%) participants had completed screening in the trial with no cancer found. 114 individuals had been referred to the MDT, of whom 72 did not have cancer. Forty two participants were diagnosed with confirmed lung cancer, 34 were detected at baseline or three months, giving a baseline prevalence of 1.7%. Thus, to date, 2.1% of all individuals screened have been diagnosed with lung cancer. 36/42 (85.7%) of the screen-detected cancers were identified at stage 1 or 2. Of those with a confirmed cancer, 17/42 (40.5%) were from the most deprived Index of Multiple Deprivation (IMD) quintile. We have defined false positives or rate of recall, as those requiring further diagnostic investigation more immediately than a repeat annual screen, but who transpired on such investigation not to have lung cancer. Thus on examining the number of UKLS participants referred to the MDT clinic, the false positive rate is 3.6% (114-42/1994); whilst the Interval Imaging Rate for the Category 3 nodules is 23.2% (472-9/1994). Psychosocial impactThe short-term and long-term psychosocial impact of participation in UKLS (trial allocation and screening result) was examined with respect to lung cancer-specific distress (primary outcome), and anxiety, depression and decision satisfaction (secondary outcomes). Trial participants were asked to complete psychosocial questionnaires at baseline consent (T0), two weeks either after randomisation to the non-screening arm or receipt of their baseline CT scan results letter (T1), and at long-term follow-up (in January 2014, 10-27 months after attending the recruitment centre) (T2).Comparison of n=2756 individuals who declined participation with n=4061 individuals who attended recruitment clinic and consented to participate indicated that women, current smokers, older individuals (aged over 70 years), those in the lowest deprivation quintile, and those with a higher affective risk perception were less likely to participate. Further study attrition was observed in the same sub-groups.Analysis of the short-term impact of trial allocation indicated that, in participants with low cancer distress at baseline, those randomised to the intervention arm reported significantly higher cancer distress at T1 follow up compared to controls (although not to clinically important levels). At T1, control arm participants were not very satisfied with their decision to take part compared to those in the intervention arm. Within the intervention arm, individuals referred to the MDT due to a major lung abnormality reported significantly greater cancer distress and anxiety at T1 follow-up than those in any other group (clear scan, significant incidental finding, or recommended for repeat scan). In the MDT referral group, the upper confidence interval for lung cancer distress approached clinically significant levels. However, those in the MDT referral group reported the highest decision satisfaction. Individuals recommended for a repeat scan reported significantly higher T1 lung cancer distress compared to the clear scan group, and had the lowest decision satisfaction of all groups. CT scan result had no impact upon depression scores at T1.Adverse psychosocial outcomes of trial allocation and screening outcome were not evident at T2 long-term follow-up. In both T0 low and high scoring participants, the difference between trial arms in T2 cancer distress levels was not statistically significant. Control group participants had significantly higher T2 anxiety and depression scores than those receiving the intervention; however, the absolute differences between trial arms were minimal and not of clinical significance. Overall, transient negative consequences were observed in individuals allocated to receive LDCT screening and in those who received unfavourable screening results, but these differences resolved over time and were not clinically significant. However, a profile of risk factors for trial non-uptake emerged which must be addressed prior to routine implementation of lung cancer screening in the UK.Health economicsDemonstrating cost effectiveness of cancer screening requires estimation of (i) net costs of screening versus detection via symptomatic presentation, (ii) impact upon (quality-adjusted) life expectancy for screened subjects, and (iii) ratio of net-benefits to net-costs incurred(incremental cost-effectivness ratio, ICER).Cost estimates were based on 2011-12 NHS tariffs. Costs incurred from UKLS are those of screening and re-screening (?282,490), diagnostic workup (?75,592), and treatment (?332,564), totalling ?690,646 (95% CI ?479,173 to ?899,794). Recruitment costs and compliance were modelled from findings of other national screening programmes (e.g. UK colorectal screening). We assumed invitation and selection (risk assessment) costs of ?10 per person, and a participation rate of 30%. The gross current costs of the programme amounted to ?754,877 (CI ?544,824 to ?966,304).The screening benefits considered were restricted to survival gains consequent upon the screen-detection of cancers. Benefits comprised life expectancy following screen-detection and treatment, minus that which would have followed eventual symptomatic presentation in the absence of screening. These expectancies were modelled from existing survival data. The estimate of survival for each cancer detected was specific to sex, age, and cancer stage, and we incorporated stage-dependent lead times of up to six years. To enable the summation of survival gains accruing at different times, we discounted future life year gains to present values at 3.5% annually. The model predicted total life-year gains of 137.2 (discounted 89.4) from detecting and treating the 42 cancers.The costs of managing these cancers which would have accrued in the absence of screening at various times in the future were offset against the gross programme costs of UKLS. These offset costs, when discounted to present values at 3.5% annually, totalled ?189,379 (CI ?152,740 to ?230,643). Net programme costs therefore amounted to ?565,498 (CI ?361,102 to ?757,762). Based on a series of assumptions used to permit exploration of cost effectiveness in this pilot study, the incremental cost effectiveness ratio (ICER) of screen-detection compared against symptomatic detection is estimated as ?6,325 per life-year gained. Further exploratory analysis, using data from previous studies to permit calculation of the cost per QALY of screening results in an estimate of around ?8,500 per QALY gained for screening, subject to a number of serious uncertainties.ConclusionsThe UKLS pilot trial has demonstrated that it would be possible to design a cost-effective programme, with minimal adverse short-term psychosocial consequences. The 60-75 year age group is most likely to benefit from population-based screening. Roll-out of screening as a service or design of a full trial would need to address issues of outreach.Lung cancer has been detected in 2.1% of people screened. The majority of screen-detected cancers were identified at stage 1 and 2, when they are potentially amenable to curative treatment, thus greater cost savings. In any service-based screening programme, uptake would differ from that seen in a trial; nevertheless trial data indicate some of the barriers to lung screening that would need to be considered. In order to maximise uptake, efficiency and cost-effectiveness of lung screening, it would be necessary to attempt to engage ‘hard-to-reach’ groups, and those who perceive themselves to be at lower risk. In particular, these groups include women, older people (71-75 years), current smokers, people of lower socioeconomic status, and those with no prior experience of lung cancer.Future work will consider the longer term outcomes of the trial participants (via ONS, HES and the cancer registration service). The UKLS data will be pooled with the NELSON and other EU trials in 2016, which will provide European mortality and cost-effectiveness data.Research should also be directed towards establishing the optimum recruitment methods, frequency of screening, the best method of reading CT scans, and the longer-term psychosocial impact.Word Count: 2400PLAIN ENGLISH SUMMARYLung cancer kills more people than any other cancer. To reduce the number of deaths, we need to detect lung cancer at an earlier stage when it can be cured. An American trial showed that chest CT screening could prevent 20% of deaths from lung cancer.In view of the above, we carried out a pilot trial of low dose CT screening for lung cancer. To find people at high risk of lung cancer, we sent postal questionnaires about known lung cancer risk factors to 250,000 people aged 50-75 years. Very few people younger than 60 years of age were at high risk. Four thousand high risk people joined the trial: half were offered a CT scan of their lungs; the others were not screened.In total, 1994 people had a CT scan; 979 of those people had clear scans, 951 needed repeat scans because of a minor change, and 64 people had major findings. Forty two cancers have been found to date, of which 36 (85.7%) were identified at an early stage, so are potentially curable. From the scans, we also found 128 people with other conditions unrelated to lung cancer, providing these people with an earlier diagnosis.To assess the emotional impact of lung screening, we looked at standard measures of cancer distress, anxiety and depression. Although some people, particularly those with an abnormal scan result, experienced more cancer distress, this was still at normal levels.Our results suggest that CT screening for lung cancer could be cost-effective, but further research is needed to confirm this.Word count: 258CHAPTER 1: INTRODUCTION1.1Lung cancer: the clinical problemLung cancer kills more people than any other cancer, both in the UK and worldwide. It accounts for 13% of all malignant diagnoses in the UK, but over 20% of all UK deaths from malignancy (and 6% of total deaths). In the UK, in 2011, there were over 43,000 lung cancer diagnoses, and nearly 35,000 lung cancer deaths. ADDIN EN.CITE <EndNote><Cite><Author>CRUK</Author><Year>2012</Year><RecNum>1</RecNum><DisplayText>(1)</DisplayText><record><rec-number>1</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>CRUK</author></authors></contributors><titles><title>Cancer Stats</title></titles><dates><year>2012</year></dates><urls><related-urls><url> Although improving, the five-year survival rate from lung cancer in the UK, for all stages, is less than 10% for both men and women; amongst the lowest for all cancer types ADDIN EN.CITE <EndNote><Cite><Author>CRUK</Author><Year>2012</Year><RecNum>1</RecNum><DisplayText>(1)</DisplayText><record><rec-number>1</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>CRUK</author></authors></contributors><titles><title>Cancer Stats</title></titles><dates><year>2012</year></dates><urls><related-urls><url> (). 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ADDIN EN.CITE.DATA 2 overall survival is best predicted by the stage of disease. Individuals with pT1aN0 have a five-year survival rate of 77%, compared with 22% for pT4Nx disease, and as low as 2% in pT4- malignant effusion and nodules. The poor survival rates in the UK are mainly due to most lung cancers being identified at a late stage, thus there is a clear rationale for seriously considering a national screening programme.The estimated cost of lung cancer to the NHS is ?9,071 per patient per year (compared with ?2,776 per patient per year for all cancer types), ADDIN EN.CITE <EndNote><Cite><Author>NCRI</Author><Year>2012</Year><RecNum>3</RecNum><DisplayText>(3)</DisplayText><record><rec-number>3</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">3</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>NCRI</author></authors></contributors><titles><title>LUNG CANCER UK PRICE TAG ECLIPSES THE COST OF ANY OTHER CANCER</title></titles><dates><year>2012</year></dates><urls><related-urls><url> and the total cost of lung cancer to the UK economy is ?2.4 billion; more than any other cancer. ADDIN EN.CITE <EndNote><Cite><Author>NCRI</Author><Year>2012</Year><RecNum>3</RecNum><DisplayText>(3)</DisplayText><record><rec-number>3</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">3</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>NCRI</author></authors></contributors><titles><title>LUNG CANCER UK PRICE TAG ECLIPSES THE COST OF ANY OTHER CANCER</title></titles><dates><year>2012</year></dates><urls><related-urls><url> This reflects the relatively high incidence of lung cancer, compounded by poor survival and high mortality. Although age-standardised lung cancer deaths have declined over the past few years, due to lower tobacco use and possibly greater public awareness, many ex-smokers remain at high risk of developing lung cancer. Their absolute risk is dependent on their age, smoking duration prior to tobacco cessation, interval since quitting and other factors such as exposure to asbestos. This group of individuals now outnumbers current smokers both in the USA and Europe, and will continue to do so over the next two to three decades. There is therefore a significant population at high risk of developing lung cancer, who could potentially benefit from screening and early detection.1.2Risk factors for lung cancerTobacco use is the major aetiological factor underlying lung cancer. The population attributable fraction for tobacco smoke (i.e. the percentage of lung cancer cases that are caused by tobacco), is 86%, representing around 34,600 cases in the UK (2010 figures).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QYXJraW48L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 9 people from the most deprived socioeconomic quintile in England are 2.5 times as likely to develop lung cancer as those from the highest quintile (after adjusting for age; 2000-2004 figures). ADDIN EN.CITE <EndNote><Cite><Author>Forman</Author><Year>2008</Year><RecNum>10</RecNum><DisplayText>(10)</DisplayText><record><rec-number>10</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">10</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Forman, D</author></authors></contributors><titles><title>Cancer Incidence by Deprivation England, 1995-2004</title><secondary-title>Cancer Publications and Reports</secondary-title></titles><dates><year>2008</year></dates><publisher>National Cancer Intelligence Network</publisher><urls><related-urls><url>.uk/view.aspx?rid=73</url></related-urls></urls></record></Cite></EndNote>10 Furthermore, this trend has been consistent since the mid-1990s. ADDIN EN.CITE <EndNote><Cite><Author>Rowan</Author><Year>2007</Year><RecNum>11</RecNum><DisplayText>(10, 11)</DisplayText><record><rec-number>11</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">11</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Rowan, S</author></authors></contributors><titles><title>Trends in cancer incidence by deprivation, England and Wales, 1990-2002</title><secondary-title>Health Stat Q</secondary-title></titles><periodical><full-title>Health Stat Q</full-title></periodical><pages>24-35</pages><volume>36</volume><dates><year>2007</year></dates><urls></urls></record></Cite><Cite><Author>Forman</Author><Year>2008</Year><RecNum>10</RecNum><record><rec-number>10</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">10</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Forman, D</author></authors></contributors><titles><title>Cancer Incidence by Deprivation England, 1995-2004</title><secondary-title>Cancer Publications and Reports</secondary-title></titles><dates><year>2008</year></dates><publisher>National Cancer Intelligence Network</publisher><urls><related-urls><url>.uk/view.aspx?rid=73</url></related-urls></urls></record></Cite></EndNote>10, 11 Recent work has supported the case that air pollution is independently associated with lung cancer.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SYWFzY2hvdS1OaWVsc2VuPC9BdXRob3I+PFllYXI+MjAx

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ADDIN EN.CITE.DATA 12 Lung cancer incidence increases with age, with around 75% of new UK cases arising in individuals aged 65 years and over (2008-2010 data). ADDIN EN.CITE <EndNote><Cite><Author>CRUK</Author><Year>2012</Year><RecNum>1</RecNum><DisplayText>(1)</DisplayText><record><rec-number>1</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>CRUK</author></authors></contributors><titles><title>Cancer Stats</title></titles><dates><year>2012</year></dates><urls><related-urls><url> Lung cancer is more common in males, with lifetime risks in the UK of around 7.1% for men and 5.6% for women. ADDIN EN.CITE <EndNote><Cite><Author>CRUK</Author><Year>2012</Year><RecNum>1</RecNum><DisplayText>(1)</DisplayText><record><rec-number>1</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>CRUK</author></authors></contributors><titles><title>Cancer Stats</title></titles><dates><year>2012</year></dates><urls><related-urls><url> models for lung cancer predictionBased on the known risk factors for lung cancer, a number of algorithmic risk models have been developed in order to predict a person’s likelihood of developing the disease. ADDIN EN.CITE <EndNote><Cite><Author>Field</Author><Year>2013</Year><RecNum>13</RecNum><DisplayText>(13)</DisplayText><record><rec-number>13</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">13</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Field, J. K.</author><author>Chen, Y.</author><author>Marcus, M. W.</author><author>McRonald, F. E.</author><author>Raji, O. Y.</author><author>Duffy, S. W.</author></authors></contributors><auth-address>Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, The University of Liverpool Cancer Research Centre, Liverpool, UK.</auth-address><titles><title>The contribution of risk prediction models to early detection of lung cancer</title><secondary-title>J Surg Oncol</secondary-title><alt-title>Journal of surgical oncology</alt-title></titles><periodical><full-title>J Surg Oncol</full-title><abbr-1>Journal of surgical oncology</abbr-1></periodical><alt-periodical><full-title>J Surg Oncol</full-title><abbr-1>Journal of surgical oncology</abbr-1></alt-periodical><pages>304-11</pages><volume>108</volume><number>5</number><edition>2013/09/03</edition><dates><year>2013</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-9098 (Electronic)&#xD;0022-4790 (Linking)</isbn><accession-num>23996507</accession-num><urls><related-urls><url> Use of such models provides the potential to target screening or resources towards those at highest risk. ADDIN EN.CITE <EndNote><Cite><Author>van Klaveren</Author><Year>2002</Year><RecNum>14</RecNum><DisplayText>(14)</DisplayText><record><rec-number>14</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">14</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>van Klaveren, R. J.</author><author>de Koning, H. J.</author><author>Mulshine, J.</author><author>Hirsch, F. R.</author></authors></contributors><auth-address>Department of Pulmonology, Erasmus Medical Centre, University Hospital Rotterdam, Dr Molewaterplein 40, 3015 GD Rotterdam, The Netherlands. vanklaveren@svlo.azr.nl</auth-address><titles><title>Lung cancer screening by spiral CT. What is the optimal target population for screening trials?</title><secondary-title>Lung Cancer</secondary-title></titles><periodical><full-title>Lung Cancer</full-title></periodical><pages>243-52</pages><volume>38</volume><number>3</number><keywords><keyword>Adult</keyword><keyword>Age Factors</keyword><keyword>Aged</keyword><keyword>Clinical Trials</keyword><keyword>Guidelines</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/epidemiology/ radiography</keyword><keyword>Mass Screening</keyword><keyword>Middle Aged</keyword><keyword>Patient Selection</keyword><keyword>Risk Assessment</keyword><keyword>Risk Factors</keyword><keyword>Smoking/adverse effects/epidemiology</keyword><keyword>Tomography, Spiral Computed</keyword></keywords><dates><year>2002</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0169-5002 (Print)</isbn><accession-num>12445745</accession-num><urls></urls></record></Cite></EndNote>14 The majority of these risk models are based predominantly upon age and smoking; these include Bach, ADDIN EN.CITE <EndNote><Cite><Author>Bach</Author><Year>2003</Year><RecNum>15</RecNum><DisplayText>(15)</DisplayText><record><rec-number>15</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">15</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bach, P. B.</author><author>Kattan, M. W.</author><author>Thornquist, M. D.</author><author>Kris, M. G.</author><author>Tate, R. C.</author><author>Barnett, M. J.</author><author>Hsieh, L. J.</author><author>Begg, C. B.</author></authors></contributors><auth-address>The Health Outcomes Research Group, Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA. bachp@</auth-address><titles><title>Variations in lung cancer risk among smokers</title><secondary-title>J Natl Cancer Inst</secondary-title></titles><periodical><full-title>J Natl Cancer Inst</full-title><abbr-1>Journal of the National Cancer Institute</abbr-1></periodical><pages>470-8</pages><volume>95</volume><number>6</number><keywords><keyword>Aged</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/ epidemiology/ethnology/ etiology/prevention &amp; control/radiography</keyword><keyword>Male</keyword><keyword>Mass Screening/ methods</keyword><keyword>Middle Aged</keyword><keyword>Multicenter Studies</keyword><keyword>Predictive Value of Tests</keyword><keyword>Randomized Controlled Trials</keyword><keyword>Risk Assessment</keyword><keyword>Risk Factors</keyword><keyword>Smoking/ adverse effects</keyword><keyword>Time Factors</keyword><keyword>Tomography, X-Ray Computed</keyword><keyword>United States/epidemiology</keyword></keywords><dates><year>2003</year><pub-dates><date>Mar 19</date></pub-dates></dates><isbn>0027-8874 (Print)</isbn><accession-num>12644540</accession-num><urls></urls></record></Cite></EndNote>15 Spitz,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TcGl0ejwvQXV0aG9yPjxZZWFyPjIwMDg8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 16 Tammemagi, ADDIN EN.CITE <EndNote><Cite><Author>Tammemagi</Author><Year>2013</Year><RecNum>17</RecNum><DisplayText>(17)</DisplayText><record><rec-number>17</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">17</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tammemagi, M. C.</author><author>Katki, H. A.</author><author>Hocking, W. G.</author><author>Church, T. R.</author><author>Caporaso, N.</author><author>Kvale, P. A.</author><author>Chaturvedi, A. K.</author><author>Silvestri, G. A.</author><author>Riley, T. L.</author><author>Commins, J.</author><author>Berg, C. D.</author></authors></contributors><auth-address>Department of Community Health Sciences, Brock University, St. Catharines, ON, Canada. martin.tammemagi@brocku.ca</auth-address><titles><title>Selection criteria for lung-cancer screening</title><secondary-title>N Engl J Med</secondary-title></titles><periodical><full-title>N Engl J Med</full-title></periodical><pages>728-36</pages><volume>368</volume><number>8</number><edition>2013/02/22</edition><keywords><keyword>Area Under Curve</keyword><keyword>Humans</keyword><keyword>Logistic Models</keyword><keyword>Lung Neoplasms/*radiography</keyword><keyword>*Mass Screening</keyword><keyword>*Patient Selection</keyword><keyword>Radiography, Thoracic</keyword><keyword>Risk Assessment/*methods</keyword><keyword>Risk Factors</keyword><keyword>Sensitivity and Specificity</keyword><keyword>*Smoking</keyword><keyword>Tomography, X-Ray Computed</keyword></keywords><dates><year>2013</year><pub-dates><date>Feb 21</date></pub-dates></dates><isbn>1533-4406 (Electronic)&#xD;0028-4793 (Linking)</isbn><accession-num>23425165</accession-num><urls><related-urls><url> and more recently that of Kovalchik.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Lb3ZhbGNoaWs8L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFy

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ADDIN EN.CITE.DATA 18 However, the predictive accuracy of lung cancer risk models may be further improved by the addition of other epidemiological risk factors. ADDIN EN.CITE <EndNote><Cite><Author>Spitz</Author><Year>2007</Year><RecNum>19</RecNum><DisplayText>(19)</DisplayText><record><rec-number>19</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">19</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Spitz, M. R.</author><author>Hong, W. K.</author><author>Amos, C. I.</author><author>Wu, X.</author><author>Schabath, M. B.</author><author>Dong, Q.</author><author>Shete, S.</author><author>Etzel, C. J.</author></authors></contributors><auth-address>Department of Epidemiology-Unit 1340, The University of Texas M. D. Anderson Cancer Center, PO Box 301439, Houston, TX 77230-1439, USA. mspitz@</auth-address><titles><title>A risk model for prediction of lung cancer</title><secondary-title>J Natl Cancer Inst</secondary-title></titles><periodical><full-title>J Natl Cancer Inst</full-title><abbr-1>Journal of the National Cancer Institute</abbr-1></periodical><pages>715-26</pages><volume>99</volume><number>9</number><keywords><keyword>Adult</keyword><keyword>European Continental Ancestry Group</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/ epidemiology/genetics/mortality</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Models, Statistical</keyword><keyword>Patient Selection</keyword><keyword>Predictive Value of Tests</keyword><keyword>Reproducibility of Results</keyword><keyword>Risk Assessment</keyword><keyword>Smoking/epidemiology</keyword><keyword>Smoking Cessation/statistics &amp; numerical data</keyword><keyword>Texas</keyword></keywords><dates><year>2007</year><pub-dates><date>May 2</date></pub-dates></dates><isbn>1460-2105 (Electronic)</isbn><accession-num>17470739</accession-num><urls></urls></record></Cite></EndNote>19 In the UK, the Liverpool Lung Project (LLP) risk model has been developed from a large case-control study of the same name.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYXNzaWR5PC9BdXRob3I+PFllYXI+MjAwODwvWWVhcj48

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ADDIN EN.CITE.DATA 20, 21 The LLP model is a robust algorithm that has been validated in two international case-control populations (Harvard and EUELC) and one independent cohort (LLP 7,500). ADDIN EN.CITE <EndNote><Cite><Author>Raji</Author><Year>2012</Year><RecNum>22</RecNum><DisplayText>(22)</DisplayText><record><rec-number>22</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">22</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Raji, Olaide Y.</author><author>Duffy, Stephen W.</author><author>Agbaje, Olorunshola F.</author><author>Baker, Stuart G.</author><author>Christiani, David C.</author><author>Cassidy, Adrian</author><author>Field, John K.</author></authors></contributors><titles><title>Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung CancerA Case–Control and Cohort Validation Study</title><secondary-title>Annals of Internal Medicine</secondary-title></titles><periodical><full-title>Annals of Internal Medicine</full-title></periodical><pages>242-250</pages><volume>157</volume><number>4</number><dates><year>2012</year></dates><isbn>0003-4819</isbn><urls><related-urls><url> The LLP risk model has distinctive strengths. Firstly, the predictor variables are all explicitly defined and can be readily assessed at the time of patient presentation, and, secondly, patients can be assigned to their appropriate risk class on the basis of information from the initial history alone. The utilisation of risk models, specifically the LLP risk model, has recently been highlighted by the National Cancer Institute (NCI).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QZXJlczwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 231.4Screening for lung cancerWorldwide, a number of cohort studies and Randomised Controlled Trials (RCTs) of screening for early detection of lung cancer have been, or are being, conducted. ADDIN EN.CITE <EndNote><Cite><Author>Field</Author><Year>2008</Year><RecNum>24</RecNum><DisplayText>(24)</DisplayText><record><rec-number>24</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">24</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Field, J. K.</author><author>Duffy, S. W.</author></authors></contributors><auth-address>Department of Surgery and Oncology, Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, Liverpool L3 9TA, UK. J.K.Field@liv.ac.uk</auth-address><titles><title>Lung cancer screening: the way forward</title><secondary-title>Br J Cancer</secondary-title><alt-title>British journal of cancer</alt-title></titles><periodical><full-title>Br J Cancer</full-title><abbr-1>British journal of cancer</abbr-1></periodical><alt-periodical><full-title>Br J Cancer</full-title><abbr-1>British journal of cancer</abbr-1></alt-periodical><pages>557-62</pages><volume>99</volume><number>4</number><edition>2008/07/31</edition><keywords><keyword>Cost-Benefit Analysis</keyword><keyword>Great Britain</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/mortality/*radiography/therapy</keyword><keyword>Mass Chest X-Ray</keyword><keyword>National Health Programs</keyword><keyword>Survival Rate</keyword><keyword>Tomography, X-Ray Computed</keyword></keywords><dates><year>2008</year><pub-dates><date>Aug 19</date></pub-dates></dates><isbn>1532-1827 (Electronic)&#xD;0007-0920 (Linking)</isbn><accession-num>18665179</accession-num><work-type>Research Support, Non-U.S. Gov&apos;t&#xD;Review</work-type><urls><related-urls><url> These have used both chest x-ray and low dose computed tomography (LDCT) screening; the latter is the superior method. ADDIN EN.CITE <EndNote><Cite><Author>Field</Author><Year>2008</Year><RecNum>24</RecNum><DisplayText>(24)</DisplayText><record><rec-number>24</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">24</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Field, J. K.</author><author>Duffy, S. W.</author></authors></contributors><auth-address>Department of Surgery and Oncology, Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, Liverpool L3 9TA, UK. J.K.Field@liv.ac.uk</auth-address><titles><title>Lung cancer screening: the way forward</title><secondary-title>Br J Cancer</secondary-title><alt-title>British journal of cancer</alt-title></titles><periodical><full-title>Br J Cancer</full-title><abbr-1>British journal of cancer</abbr-1></periodical><alt-periodical><full-title>Br J Cancer</full-title><abbr-1>British journal of cancer</abbr-1></alt-periodical><pages>557-62</pages><volume>99</volume><number>4</number><edition>2008/07/31</edition><keywords><keyword>Cost-Benefit Analysis</keyword><keyword>Great Britain</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/mortality/*radiography/therapy</keyword><keyword>Mass Chest X-Ray</keyword><keyword>National Health Programs</keyword><keyword>Survival Rate</keyword><keyword>Tomography, X-Ray Computed</keyword></keywords><dates><year>2008</year><pub-dates><date>Aug 19</date></pub-dates></dates><isbn>1532-1827 (Electronic)&#xD;0007-0920 (Linking)</isbn><accession-num>18665179</accession-num><work-type>Research Support, Non-U.S. Gov&apos;t&#xD;Review</work-type><urls><related-urls><url> radiography & sputum cytology lung cancer screeningThe earliest lung screening trial was undertaken in London in the 1960s: over 55,000 individuals were randomised to either chest radiography every six months for three years, or chest radiography at the beginning and end of the three year period. ADDIN EN.CITE <EndNote><Cite><Author>Brett</Author><Year>1969</Year><RecNum>25</RecNum><DisplayText>(25)</DisplayText><record><rec-number>25</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">25</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Brett, G. Z.</author></authors></contributors><titles><title>Earlier diagnosis and survival in lung cancer</title><secondary-title>Br Med J</secondary-title></titles><periodical><full-title>Br Med J</full-title></periodical><pages>260-2</pages><volume>4</volume><number>5678</number><edition>1969/11/01</edition><keywords><keyword>Adenocarcinoma/mortality</keyword><keyword>Adult</keyword><keyword>Age Factors</keyword><keyword>Aged</keyword><keyword>Carcinoma, Squamous Cell/mortality</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/*mortality/*radiography</keyword><keyword>Male</keyword><keyword>*Mass Chest X-Ray</keyword><keyword>Middle Aged</keyword><keyword>Prognosis</keyword><keyword>Statistics as Topic</keyword><keyword>Time Factors</keyword></keywords><dates><year>1969</year><pub-dates><date>Nov 1</date></pub-dates></dates><isbn>0007-1447 (Print)</isbn><accession-num>5345935</accession-num><urls><related-urls><url> No mortality difference was found between the two groups. 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ADDIN EN.CITE.DATA 26, 271.4.2Low dose computed tomography lung cancer screeningLDCT, which was introduced in the late 1990s, offers a major advance in imaging technology. ADDIN EN.CITE <EndNote><Cite><Author>Reeves</Author><Year>2000</Year><RecNum>28</RecNum><DisplayText>(28)</DisplayText><record><rec-number>28</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">28</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Reeves, A. P.</author><author>Kostis, W. J.</author></authors></contributors><auth-address>School of Electrical Engineering, Cornell University, Ithaca, New York, USA. reeves@ee.cornell.edu</auth-address><titles><title>Computer-aided diagnosis for lung cancer</title><secondary-title>Radiol Clin North Am</secondary-title></titles><periodical><full-title>Radiol Clin North Am</full-title></periodical><pages>497-509</pages><volume>38</volume><number>3</number><keywords><keyword>Coin Lesion, Pulmonary/ radiography</keyword><keyword>Diagnosis, Computer-Assisted</keyword><keyword>Humans</keyword><keyword>Image Processing, Computer-Assisted</keyword><keyword>Lung Neoplasms/ radiography</keyword><keyword>Radiography, Thoracic</keyword><keyword>Tomography, X-Ray Computed</keyword></keywords><dates><year>2000</year><pub-dates><date>May</date></pub-dates></dates><isbn>0033-8389 (Print)</isbn><accession-num>10855257</accession-num><urls></urls></record></Cite></EndNote>28 LDCT is more sensitive than chest radiography, and has enabled detection of lung tumours smaller than one centimetre. Early studies using this technology include the USA-based Early Lung Cancer Action Project (ELCAP) ADDIN EN.CITE <EndNote><Cite><Author>Yankelevitz</Author><Year>2000</Year><RecNum>29</RecNum><DisplayText>(29)</DisplayText><record><rec-number>29</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">29</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Yankelevitz, D. F.</author><author>Reeves, A. P.</author><author>Kostis, W. J.</author><author>Zhao, B.</author><author>Henschke, C. I.</author></authors></contributors><auth-address>Department of Radiology, New York Presbyterian Hospital-Weill Cornell Medical Center, Building Starr-8A23, 525 E 68th St, New York, NY 10021, USA. dyankele@mail.med.cornell.edu</auth-address><titles><title>Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation</title><secondary-title>Radiology</secondary-title></titles><periodical><full-title>Radiology</full-title></periodical><pages>251-6</pages><volume>217</volume><number>1</number><keywords><keyword>Aged</keyword><keyword>Coin Lesion, Pulmonary/ pathology/ radiography</keyword><keyword>Diagnosis, Differential</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Image Processing, Computer-Assisted</keyword><keyword>Lung Neoplasms/pathology/radiography</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Phantoms, Imaging</keyword><keyword>Tomography, X-Ray Computed</keyword></keywords><dates><year>2000</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>0033-8419 (Print)</isbn><accession-num>11012453</accession-num><urls></urls></record></Cite></EndNote>29 in 1,000 high-risk smokers; the Mayo Clinic project in 1,520 individuals, including annual sputum cytology, ADDIN EN.CITE <EndNote><Cite><Author>Swensen</Author><Year>2000</Year><RecNum>30</RecNum><DisplayText>(30)</DisplayText><record><rec-number>30</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">30</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Swensen, S. J.</author><author>Viggiano, R. W.</author><author>Midthun, D. E.</author><author>Muller, N. L.</author><author>Sherrick, A.</author><author>Yamashita, K.</author><author>Naidich, D. P.</author><author>Patz, E. F.</author><author>Hartman, T. E.</author><author>Muhm, J. R.</author><author>Weaver, A. L.</author></authors></contributors><auth-address>Dept of Diagnostic Radiology, Mayo Clinic and Mayo Foundation, Rochester, MN 55905, USA.</auth-address><titles><title>Lung nodule enhancement at CT: multicenter study</title><secondary-title>Radiology</secondary-title></titles><periodical><full-title>Radiology</full-title></periodical><pages>73-80</pages><volume>214</volume><number>1</number><keywords><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Aged, 80 and over</keyword><keyword>Coin Lesion, Pulmonary/ radiography</keyword><keyword>Contrast Media</keyword><keyword>Diagnosis, Differential</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/ radiography</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Prospective Studies</keyword><keyword>Radiographic Image Enhancement</keyword><keyword>Sensitivity and Specificity</keyword><keyword>Tomography, X-Ray Computed</keyword></keywords><dates><year>2000</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0033-8419 (Print)</isbn><accession-num>10644104</accession-num><urls></urls></record></Cite></EndNote>30 the Milan studyPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QYXN0b3Jpbm88L0F1dGhvcj48WWVhcj4yMDAzPC9ZZWFy

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ADDIN EN.CITE.DATA 31 and a three-year mass screening programme using a mobile CT unit in Japan. ADDIN EN.CITE <EndNote><Cite><Author>Sone</Author><Year>1998</Year><RecNum>32</RecNum><DisplayText>(32)</DisplayText><record><rec-number>32</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">32</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sone, S.</author><author>Takashima, S.</author><author>Li, F.</author><author>Yang, Z.</author><author>Honda, T.</author><author>Maruyama, Y.</author><author>Hasegawa, M.</author><author>Yamanda, T.</author><author>Kubo, K.</author><author>Hanamura, K.</author><author>Asakura, K.</author></authors></contributors><auth-address>Department of Radiology, Shinshu University School of Medicine, Matsumoto, Japan. shusone@gipac.shinshu-u.ac.jp</auth-address><titles><title>Mass screening for lung cancer with mobile spiral computed tomography scanner</title><secondary-title>Lancet</secondary-title></titles><periodical><full-title>Lancet</full-title></periodical><pages>1242-5</pages><volume>351</volume><number>9111</number><keywords><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung/radiography</keyword><keyword>Lung Neoplasms/ prevention &amp; control/radiography</keyword><keyword>Male</keyword><keyword>Mass Screening</keyword><keyword>Middle Aged</keyword><keyword>Mobile Health Units</keyword><keyword>Radiography, Thoracic</keyword><keyword>Tomography Scanners, X-Ray Computed</keyword><keyword>Tomography, X-Ray Computed</keyword></keywords><dates><year>1998</year><pub-dates><date>Apr 25</date></pub-dates></dates><isbn>0140-6736 (Print)</isbn><accession-num>9643744</accession-num><urls></urls></record></Cite></EndNote>32 The ELCAP (observational) study was later expanded to an international collaboration including 30,000 subjects. The EU-US Spiral CT Collaboration was initiated in 2001 in Liverpool. Subsequent meetings throughout Europe resulted in the development of collaborative protocols to provide a mechanism for different trial groups to work together, and for outcome data to be comparable between studies. The ultimate aim – pooling of results – was formalised in the ‘Liverpool Statement 2005’.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GaWVsZDwvQXV0aG9yPjxZZWFyPjIwMDY8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 26 trial. NLST reported a 20% relative reduction in lung cancer mortality in the LDCT arm. ADDIN EN.CITE <EndNote><Cite><Author>National Lung Screening Trial Research Team</Author><Year>2011</Year><RecNum>34</RecNum><DisplayText>(35)</DisplayText><record><rec-number>34</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">34</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>National Lung Screening Trial Research Team, </author><author>Aberle, D.R.</author><author>Adams, A.M.</author><author>Berg, C.D.</author><author>Black, W.C.</author><author>Clapp, J.D.</author><author>Fagerstrom,R.M.</author><author>Gareen, I.F.</author><author>Gatsonis, C.</author><author>Marcus, P.M.</author><author>Sicks. J.D. </author></authors></contributors><titles><title>Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening</title><secondary-title>N Engl J Med</secondary-title></titles><periodical><full-title>N Engl J Med</full-title></periodical><edition>2011/07/01</edition><dates><year>2011</year><pub-dates><date>Jun 29</date></pub-dates></dates><isbn>1533-4406 (Electronic)&#xD;0028-4793 (Linking)</isbn><accession-num>21714641</accession-num><urls><related-urls><url> Furthermore, all-cause mortality was reduced by 6.7% in the low-dose CT group compared with the X-ray group.1.4.2.2European lung cancer screening trialsIn Europe, the Dutch-Belgian randomised LDCT lung cancer screening trial (NELSON) was launched in 2003 and is currently under way.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 37-39 and the French randomised pilot study, Depiscan. ADDIN EN.CITE <EndNote><Cite><Author>Blanchon</Author><Year>2007</Year><RecNum>39</RecNum><DisplayText>(40)</DisplayText><record><rec-number>39</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">39</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Blanchon, T.</author><author>Brechot, J. M.</author><author>Grenier, P. A.</author><author>Ferretti, G. R.</author><author>Lemarie, E.</author><author>Milleron, B.</author><author>Chague, D.</author><author>Laurent, F.</author><author>Martinet, Y.</author><author>Beigelman-Aubry, C.</author><author>Blanchon, F.</author><author>Revel, M. P.</author><author>Friard, S.</author><author>Remy-Jardin, M.</author><author>Vasile, M.</author><author>Santelmo, N.</author><author>Lecalier, A.</author><author>Lefebure, P.</author><author>Moro-Sibilot, D.</author><author>Breton, J. L.</author><author>Carette, M. F.</author><author>Brambilla, C.</author><author>Fournel, F.</author><author>Kieffer, A.</author><author>Frija, G.</author><author>Flahault, A.</author></authors></contributors><auth-address>INSERM, U707, F-75012 Paris, France. blanchon@u707.jussieu.fr</auth-address><titles><title>Baseline results of the Depiscan study: a French randomized pilot trial of lung cancer screening comparing low dose CT scan (LDCT) and chest X-ray (CXR)</title><secondary-title>Lung Cancer</secondary-title></titles><periodical><full-title>Lung Cancer</full-title></periodical><pages>50-8</pages><volume>58</volume><number>1</number><dates><year>2007</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>0169-5002 (Print)</isbn><accession-num>17624475</accession-num><urls></urls></record></Cite></EndNote>40 The ItaLung, MILD and DLCST have all reported on their mortality data, which showed no difference in the CT screened arm; however none of the studies were powered to provide such a result.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Mb3BlcyBQZWduYTwvQXV0aG9yPjxZZWFyPjIwMDk8L1ll

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ADDIN EN.CITE.DATA 41-43 A summary of the European lung cancer screening trials is shown in Table 1.An overview of the European randomised lung cancer CT screening trials (EUCT) was prepared by the seven contributing PIs, and this paper also discussed the implementation of CT screening in Europe, post NLST. ADDIN EN.CITE <EndNote><Cite><Author>Field</Author><Year>2013</Year><RecNum>43</RecNum><DisplayText>(44)</DisplayText><record><rec-number>43</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">43</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Field, J. K.</author><author>van Klaveren, R.</author><author>Pedersen, J. H.</author><author>Pastorino, U.</author><author>Paci, E.</author><author>Becker, N.</author><author>Infante, M.</author><author>Oudkerk, M.</author><author>de Koning, H. J.</author><author>European Randomized Screening Trial, Group</author></authors></contributors><auth-address>The University of Liverpool Cancer Research Centre, Liverpool, UK.</auth-address><titles><title>European randomized lung cancer screening trials: Post NLST</title><secondary-title>J Surg Oncol</secondary-title><alt-title>Journal of surgical oncology</alt-title></titles><periodical><full-title>J Surg Oncol</full-title><abbr-1>Journal of surgical oncology</abbr-1></periodical><alt-periodical><full-title>J Surg Oncol</full-title><abbr-1>Journal of surgical oncology</abbr-1></alt-periodical><pages>280-6</pages><volume>108</volume><number>5</number><keywords><keyword>Aged</keyword><keyword>Automatic Data Processing</keyword><keyword>*Early Detection of Cancer</keyword><keyword>Europe</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/*diagnosis/mortality</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>*Randomized Controlled Trials as Topic</keyword></keywords><dates><year>2013</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-9098 (Electronic)&#xD;0022-4790 (Linking)</isbn><accession-num>23893464</accession-num><urls><related-urls><url> The overview was based on the epidemiological, radiological, and nodule management aspects of their trials at August 2010,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GaWVsZDwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 36 trial has reported in 2015.Table 1: Overview of the European randomised lung cancer CT screening trials, based on the 2010 data; adapted from Field et al. ADDIN EN.CITE <EndNote><Cite><Author>Field</Author><Year>2013</Year><RecNum>1049</RecNum><DisplayText>(46)</DisplayText><record><rec-number>1049</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1049</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Field, J. K.</author><author>Hansell, D. M.</author><author>Duffy, S. W.</author><author>Baldwin, D. R.</author></authors></contributors><auth-address>Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, Liverpool, UK. Electronic address: J.K.Field@liv.ac.uk.</auth-address><titles><title>CT screening for lung cancer: countdown to implementation</title><secondary-title>Lancet Oncol</secondary-title><alt-title>The lancet oncology</alt-title></titles><periodical><full-title>Lancet Oncol</full-title><abbr-1>The lancet oncology</abbr-1></periodical><alt-periodical><full-title>Lancet Oncol</full-title><abbr-1>The lancet oncology</abbr-1></alt-periodical><pages>e591-600</pages><volume>14</volume><number>13</number><dates><year>2013</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>1474-5488 (Electronic)&#xD;1470-2045 (Linking)</isbn><accession-num>24275132</accession-num><urls><related-urls><url>(13)70293-6</electronic-resource-num></record></Cite></EndNote>46 NELSON= Dutch-Belgian lung cancer screening trial. DLCST = Danish lung cancer screening trial. LUSI = German Lung Cancer Screening Intervention Study. ITALUNG = Italian Lung cancer Computed Tomography screening trial. DANTE = Detection and screening of early lung cancer by Novel imaging Technology and molecular assays. MILD = Multi-centric Italian Lung Detection Trial. Data are correct to August 2010. **Cut off date 1st January 2011.Trial NameNELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 48 but even when the results of NLST are combined with the three European trials that have reported on mortality (ItaLung, MILD, DLCST), there is still a 19% reduction. ADDIN EN.CITE <EndNote><Cite><Author>Field</Author><Year>2013</Year><RecNum>1049</RecNum><DisplayText>(46)</DisplayText><record><rec-number>1049</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1049</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Field, J. K.</author><author>Hansell, D. M.</author><author>Duffy, S. W.</author><author>Baldwin, D. R.</author></authors></contributors><auth-address>Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, Liverpool, UK. Electronic address: J.K.Field@liv.ac.uk.</auth-address><titles><title>CT screening for lung cancer: countdown to implementation</title><secondary-title>Lancet Oncol</secondary-title><alt-title>The lancet oncology</alt-title></titles><periodical><full-title>Lancet Oncol</full-title><abbr-1>The lancet oncology</abbr-1></periodical><alt-periodical><full-title>Lancet Oncol</full-title><abbr-1>The lancet oncology</abbr-1></alt-periodical><pages>e591-600</pages><volume>14</volume><number>13</number><dates><year>2013</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>1474-5488 (Electronic)&#xD;1470-2045 (Linking)</isbn><accession-num>24275132</accession-num><urls><related-urls><url>(13)70293-6</electronic-resource-num></record></Cite></EndNote>46 If we assume that LDCT screening does reduce mortality, it is then necessary to demonstrate that this can be achieved in a cost effective manner. This in turn requires information about benefits and harms as well as the optimum way to select and recruit individuals at risk. The final results of NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 study has adequate power to demonstrate a substantial benefit in a high risk group.The UK National Screening Committee has determined 22 criteria for the viability, effectiveness and appropriateness of a screening programme, ADDIN EN.CITE <EndNote><Cite><RecNum>49</RecNum><DisplayText>(49)</DisplayText><record><rec-number>49</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">49</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors></contributors><titles><title>UK National Screening Committee: Criteria for appraising the viability, effectiveness and appropriateness of a screening programme</title></titles><dates></dates><publisher>Public Health England</publisher><urls><related-urls><url> 20 of which are relevant to LDCT lung cancer screening. Black et al ADDIN EN.CITE <EndNote><Cite><Author>Black</Author><Year>2007</Year><RecNum>50</RecNum><DisplayText>(50)</DisplayText><record><rec-number>50</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">50</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Black, C.</author><author>de Verteuil, R.</author><author>Walker, S.</author><author>Ayres, J.</author><author>Boland, A.</author><author>Bagust, A.</author><author>Waugh, N.</author></authors></contributors><auth-address>Aberdeen Health Technology Assessment Group, Department of Public Health, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, UK. Corri.black@abdn.ac.uk</auth-address><titles><title>Population screening for lung cancer using computed tomography, is there evidence of clinical effectiveness? A systematic review of the literature</title><secondary-title>Thorax</secondary-title></titles><periodical><full-title>Thorax</full-title><abbr-1>Thorax</abbr-1></periodical><pages>131-8</pages><volume>62</volume><number>2</number><keywords><keyword>Humans</keyword><keyword>Lung Neoplasms/mortality/prevention &amp; control/ radiography</keyword><keyword>Mass Screening/ standards</keyword><keyword>Survival Rate</keyword><keyword>Tomography, X-Ray Computed/standards</keyword><keyword>Treatment Outcome</keyword></keywords><dates><year>2007</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0040-6376 (Print)</isbn><accession-num>17287305</accession-num><urls></urls></record></Cite></EndNote>50 have undertaken a systematic review of the literature in order to ascertain whether there was evidence for any clinical effectiveness utilising LDCT for lung cancer screening. This review was undertaken at a time when there was a paucity of real data and thus the conclusions were drawn from two small trials with very variable results. Not surprisingly, their conclusion stated that there was insufficient evidence at the time to support LDCT screening. As part of feasibility studies prior to the start of the UKLS trial, a cost effectiveness model was developed by Professor David Whynes, utilising the most conservative costings within the UK and NHS tariffs. This estimated the incremental cost effectiveness ratio of a single screen amongst a high-risk male population to be around ?14,000 per quality-adjusted life year (QALY) gained. ADDIN EN.CITE <EndNote><Cite><Author>Whynes</Author><Year>2008</Year><RecNum>1046</RecNum><DisplayText>(51)</DisplayText><record><rec-number>1046</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1046</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Whynes, D. K.</author></authors></contributors><titles><title>Could CT screening for lung cancer ever be cost effective in the United Kingdom ?</title><secondary-title>Cost Eff Resour Alloc</secondary-title></titles><periodical><full-title>Cost Eff Resour Alloc</full-title></periodical><pages>5</pages><volume>6</volume><number>1</number><dates><year>2008</year><pub-dates><date>Feb 26</date></pub-dates></dates><accession-num>18302756</accession-num><urls><related-urls><url> </url></related-urls></urls></record></Cite></EndNote>511.6The United Kingdom Lung Cancer Screening Trial To begin to address some of these questions, the National Institute for Health Research Health Technology Assessment (NIHR HTA) programme provided funding for a pilot study of LDCT screening in the UK population. Successful and cost-effective screening for lung cancer relies firstly upon identifying and targeting high risk individuals, ADDIN EN.CITE <EndNote><Cite><Author>van Klaveren</Author><Year>2002</Year><RecNum>14</RecNum><DisplayText>(14)</DisplayText><record><rec-number>14</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">14</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>van Klaveren, R. J.</author><author>de Koning, H. J.</author><author>Mulshine, J.</author><author>Hirsch, F. R.</author></authors></contributors><auth-address>Department of Pulmonology, Erasmus Medical Centre, University Hospital Rotterdam, Dr Molewaterplein 40, 3015 GD Rotterdam, The Netherlands. vanklaveren@svlo.azr.nl</auth-address><titles><title>Lung cancer screening by spiral CT. What is the optimal target population for screening trials?</title><secondary-title>Lung Cancer</secondary-title></titles><periodical><full-title>Lung Cancer</full-title></periodical><pages>243-52</pages><volume>38</volume><number>3</number><keywords><keyword>Adult</keyword><keyword>Age Factors</keyword><keyword>Aged</keyword><keyword>Clinical Trials</keyword><keyword>Guidelines</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/epidemiology/ radiography</keyword><keyword>Mass Screening</keyword><keyword>Middle Aged</keyword><keyword>Patient Selection</keyword><keyword>Risk Assessment</keyword><keyword>Risk Factors</keyword><keyword>Smoking/adverse effects/epidemiology</keyword><keyword>Tomography, Spiral Computed</keyword></keywords><dates><year>2002</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0169-5002 (Print)</isbn><accession-num>12445745</accession-num><urls></urls></record></Cite></EndNote>14 the US and European trials have used a combination of age and smoking history as trial eligibility criteria. However, as noted above, a number of more formal lung cancer risk prediction algorithms have been developed, ADDIN EN.CITE <EndNote><Cite><Author>Field</Author><Year>2013</Year><RecNum>13</RecNum><DisplayText>(13)</DisplayText><record><rec-number>13</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">13</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Field, J. K.</author><author>Chen, Y.</author><author>Marcus, M. W.</author><author>McRonald, F. E.</author><author>Raji, O. Y.</author><author>Duffy, S. W.</author></authors></contributors><auth-address>Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, The University of Liverpool Cancer Research Centre, Liverpool, UK.</auth-address><titles><title>The contribution of risk prediction models to early detection of lung cancer</title><secondary-title>J Surg Oncol</secondary-title><alt-title>Journal of surgical oncology</alt-title></titles><periodical><full-title>J Surg Oncol</full-title><abbr-1>Journal of surgical oncology</abbr-1></periodical><alt-periodical><full-title>J Surg Oncol</full-title><abbr-1>Journal of surgical oncology</abbr-1></alt-periodical><pages>304-11</pages><volume>108</volume><number>5</number><edition>2013/09/03</edition><dates><year>2013</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1096-9098 (Electronic)&#xD;0022-4790 (Linking)</isbn><accession-num>23996507</accession-num><urls><related-urls><url> and these may have utility in selecting those people who are suitable for screening. ADDIN EN.CITE <EndNote><Cite><Author>van Klaveren</Author><Year>2002</Year><RecNum>14</RecNum><DisplayText>(14)</DisplayText><record><rec-number>14</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">14</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>van Klaveren, R. J.</author><author>de Koning, H. J.</author><author>Mulshine, J.</author><author>Hirsch, F. R.</author></authors></contributors><auth-address>Department of Pulmonology, Erasmus Medical Centre, University Hospital Rotterdam, Dr Molewaterplein 40, 3015 GD Rotterdam, The Netherlands. vanklaveren@svlo.azr.nl</auth-address><titles><title>Lung cancer screening by spiral CT. What is the optimal target population for screening trials?</title><secondary-title>Lung Cancer</secondary-title></titles><periodical><full-title>Lung Cancer</full-title></periodical><pages>243-52</pages><volume>38</volume><number>3</number><keywords><keyword>Adult</keyword><keyword>Age Factors</keyword><keyword>Aged</keyword><keyword>Clinical Trials</keyword><keyword>Guidelines</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/epidemiology/ radiography</keyword><keyword>Mass Screening</keyword><keyword>Middle Aged</keyword><keyword>Patient Selection</keyword><keyword>Risk Assessment</keyword><keyword>Risk Factors</keyword><keyword>Smoking/adverse effects/epidemiology</keyword><keyword>Tomography, Spiral Computed</keyword></keywords><dates><year>2002</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0169-5002 (Print)</isbn><accession-num>12445745</accession-num><urls></urls></record></Cite></EndNote>14 In the UK, the risk model of choice is the Liverpool Lung Project (LLP) algorithm,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYXNzaWR5PC9BdXRob3I+PFllYXI+MjAwNjwvWWVhcj48

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ADDIN EN.CITE.DATA 52 However, as only a pilot UKLS trial was undertaken, the data for mortality assessment can only be calculated when pooled with the other EU screening trials. The UKLS incorporates a number of other elements that are specific to the area of lung cancer screening RCTs. Firstly, UKLS is a population-based study, which randomly approached 250,000 individuals aged 50-75 years through local Primary Care Trust (PCT) records. Secondly, UKLS employs a validated lung cancer risk prediction model at an individual level ADDIN EN.CITE <EndNote><Cite><Author>Raji</Author><Year>2012</Year><RecNum>22</RecNum><DisplayText>(22)</DisplayText><record><rec-number>22</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">22</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Raji, Olaide Y.</author><author>Duffy, Stephen W.</author><author>Agbaje, Olorunshola F.</author><author>Baker, Stuart G.</author><author>Christiani, David C.</author><author>Cassidy, Adrian</author><author>Field, John K.</author></authors></contributors><titles><title>Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung CancerA Case–Control and Cohort Validation Study</title><secondary-title>Annals of Internal Medicine</secondary-title></titles><periodical><full-title>Annals of Internal Medicine</full-title></periodical><pages>242-250</pages><volume>157</volume><number>4</number><dates><year>2012</year></dates><isbn>0003-4819</isbn><urls><related-urls><url> to identify high risk subjects; thus targeting resources to those most likely to benefit from screening. Thirdly, UKLS is unique in having a formal care pathway plan, based on categorisation (size, volume and growth) of lung nodules.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYWxkd2luPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48

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ADDIN EN.CITE.DATA 36The UKLS pilot trial focused on two geographical areas, covering six PCTs around Liverpool and Cambridge. These areas were selected as they both have a specialist thoracic hospital (Liverpool Heart and Chest Hospital and Papworth Hospital, respectively). Additionally, they cover very different populations in disparate areas of England, encompassing a broad range of socioeconomic groups that are representative of the UK as a whole. Liverpool is a large coastal port with an industrial heritage, and has the highest incidence of lung cancer in England, together with a very high risk community within the region. Papworth is located in a rural area (individuals with lower risk of lung cancer but greater distances to travel for screening) but has adjacent cities whose populations are at significant risk of developing lung cancer, albeit lower than that of Liverpool.1.6.1Rationale for UKLS pilot trialBy combining results of the UKLS pilot trial with those from NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 and other European studies, it will be possible to model any likely mortality benefit of LDCT screening and treatment of early lesions. The pilot trial will also enable assessment of screening uptake, and factors that are unique to the UK population and the NHS. This will include testing the intervention against the criteria outlined by the UK Screening Committee, especially some of those criteria concerning cost-effectiveness. The potential cost-effectiveness of UKLS has been maximised by the trial design, i.e. selection of a population at sufficiently high risk to yield a substantial number of tumours in return for the screening activity. ADDIN EN.CITE <EndNote><Cite><Author>van Klaveren</Author><Year>2002</Year><RecNum>14</RecNum><DisplayText>(14)</DisplayText><record><rec-number>14</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">14</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>van Klaveren, R. J.</author><author>de Koning, H. J.</author><author>Mulshine, J.</author><author>Hirsch, F. R.</author></authors></contributors><auth-address>Department of Pulmonology, Erasmus Medical Centre, University Hospital Rotterdam, Dr Molewaterplein 40, 3015 GD Rotterdam, The Netherlands. vanklaveren@svlo.azr.nl</auth-address><titles><title>Lung cancer screening by spiral CT. What is the optimal target population for screening trials?</title><secondary-title>Lung Cancer</secondary-title></titles><periodical><full-title>Lung Cancer</full-title></periodical><pages>243-52</pages><volume>38</volume><number>3</number><keywords><keyword>Adult</keyword><keyword>Age Factors</keyword><keyword>Aged</keyword><keyword>Clinical Trials</keyword><keyword>Guidelines</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/epidemiology/ radiography</keyword><keyword>Mass Screening</keyword><keyword>Middle Aged</keyword><keyword>Patient Selection</keyword><keyword>Risk Assessment</keyword><keyword>Risk Factors</keyword><keyword>Smoking/adverse effects/epidemiology</keyword><keyword>Tomography, Spiral Computed</keyword></keywords><dates><year>2002</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0169-5002 (Print)</isbn><accession-num>12445745</accession-num><urls></urls></record></Cite></EndNote>14 However, as the pilot trial is insufficiently powered to demonstrate a reduction in mortality, and a decision has been made not to fund the full UKLS trial, cost-effectiveness will have to be modelled.1.6.2Overall Aim of the UKLS TrialThe overall aim of the UKLS pilot trial is to contribute to the data required for an informed decision to be made regarding the introduction of population screening for lung cancer. This involves determining the best recruitment and screening strategies, introducing a screening protocol for the management of CT detected nodules, establishing that the individuals selected for screening are at a sufficiently high risk for the benefits of the screening to outweigh the harms, and assessing the physical and psychological consequences and the health economic implications of screening. Further objectives are to create a resource for future improvements to screening strategies and to provide data for the EU lung cancer pooling to ascertain European mortality.CHAPTER 2: METHODS2.1ObjectivesThe primary objective of this study was to carry out a pilot RCT examining the use of 16 channel (or higher) multi-detector CT (MDCT) in the screened group to identify early lung cancer. As UKLS is a pilot study, the main objectives are to provide data to facilitate decision making by funders and policy makers regarding the possibility of future introduction of lung cancer screening into the NHS. At this stage, the trial does not have sufficient power to draw any definitive conclusions regarding clinical or mortality benefits, or to provide categorical health economic impact figures. However, it is possible to use the pilot data in algorithms to model the likely clinical and economic impact of a larger scale screening project. The pilot study has also been designed to monitor the performance of all aspects of the clinical care pathway and the research evaluation technicalities, including a bespoke web-based UKLS database.2.2Trial designThe UKLS is a RCT of LDCT versus usual care, for the early detection of lung cancer. The methods for the UKLS pilot study were derived from an initial feasibility study, and follow the Wald Single-Screen Design.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYWxkd2luPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48

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ADDIN EN.CITE.DATA 36 and this will allow a combined analysis of results to increase the statistical power of both trials.Two main components were used in UKLS: an initial questionnaire-based screen to identify high risk individuals from the population, followed by a RCT with intervention (LDCT) and control arms. Participants were allocated randomly to trial arms in a 1:1 ratio. Figure 1 illustrates the trial recruitment process. The UKLS trial design, patient information booklet and questionnaires were discussed with our patient representatives at each stage of the development of the trial.Figure 1: UKLS Trial Recruitment and implementation process2.2.1Ethical approval and research governanceThe study received approval from the National Information Governance Board (NIGB). Ethical approval for the study was given by Liverpool Central Research Ethics Committee in December 2010 (reference number 10/H1005/74. Site specific assessments were obtained from the Primary Care Trusts (PCTs) covering Liverpool, Sefton and Knowsley in the North and Bedfordshire, Cambridgeshire and Peterborough in the South. The trial was registered with the International Standard Randomised Controlled Trial Register (ISRCTN) under the reference 78513845. A summary of the changes made to the original protocol is given in Appendix 1. Additionally, as fewer people than anticipated were at high risk of lung cancer (and therefore eligible for the trial), two alterations were made to the original plan. Firstly, even though the response rate to the initial UKLS questionnaire was at the expected level, the anticipated proportion of responders with a risk of >5% over a five year period was far lower than expected. This observation may indicate that lower risk people are more health conscious or simply more willing to participate in trials. Thus in order to achieve the 4,000 high risk individuals, the number of population approaches was increased from 82,000 to 250,000 (this is expanded on further in the section on Sample Size). Secondly, the Liverpool Lung Project (LLP) risk assessment model (used to determine eligibility for the trial) was adapted to include in the risk calculation a history of respiratory diseases (COPD, tuberculosis, bronchitis and emphysema) as well as pneumonia. Additionally, for the purposes of calculating lung cancer risk, a history of pipe or cigar smoking was classified in the same way as cigarette smoking. The refined model is known as LLPv2 (see Appendix 2). 2.3Participants2.3.1Eligibility criteriaThe eligibility criteria for trial participants are shown below.2.3.1.1Inclusion criteriaFive-year lung cancer risk >5%, based on the LLPv2 Risk Prediction Model (includes age, sex, smoking duration (cigarettes, pipe and cigars), previous history of respiratory diseases (COPD, emphysema, bronchitis, pneumonia, tuberculosis), history of previous cancer, family history (early/late onset), exposure to asbestos)Males and females aged between 50 to 75 years oldFully informed written consent given2.3.1.2Exclusion criteriaUnable to give consent Co-morbidity which would unequivocally contraindicate either screening or treatment if lung cancer were detected A CT scan of the chest performed within the year preceding the invitation to be screenedAny condition precluding written informed consentInability to lie flat2.3.2Setting and population approaches As UKLS is a population study, its starting point was NHS Primary Care Trust (PCT) records covering regions in the vicinity of two thoracic hospitals specialising in lung cancer imaging, pathology and surgery: Liverpool Heart and Chest Hospital, and Papworth Hospital, Cambridgeshire. Records relating to 249,988 individuals aged 50-75 years residing in specific PCT areas (Liverpool, Knowsley, Sefton, Cambridgeshire, Peterborough and Bedfordshire) were provided by the PCTs to a third party data management company (Radar). Following exclusions by the PCTs, 247,354 of these individuals were approached by post by Radar. Approaches took place over two time periods: August 2011 – March 2012, and May – August 2012.The initial contact from the data management company took the form of an invitation letter (on the respective PCT-headed notepaper), a UKLS participant information sheet and Questionnaire 1 (see Appendix 3). This questionnaire covered smoking history and duration, personal history of non-malignant lung diseases (e.g. pneumonia, COPD, emphysema, bronchitis, tuberculosis) and previous malignancy, exposure to asbestos, and family history of lung and other cancers; in addition, it enquired whether the individual would be interested in participating in a screening study. For those who were unwilling to complete the entire questionnaire, and who were not interested in participating further, there was a shorter non-participation questionnaire (covering smoking status, lung cancer prior experience and concern, and educational level), which they were asked to return instead (see Appendix 3).2.3.3Response categorisationApproached subjects were categorised as follows, based on their response to the first invitation: Positive Responders: individuals who returned Questionnaire 1, and agreed to participate in UKLS; Negative Responders: individuals who declined to participate in UKLS, but supplied some basic information by completing the shorter, non-participation questionnaire; Non-responders: individuals who did not respond to the first invitation.2.3.4Risk assessment/eligibility for trialCompleted Questionnaire 1s (from positive responders) were returned to the data management company, scanned, and the data analysed automatically in order to identify individuals at high risk of developing lung cancer over the next five years (LLP risk score ≥ 5).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYXNzaWR5PC9BdXRob3I+PFllYXI+MjAwODwvWWVhcj48

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ADDIN EN.CITE.DATA 20 A modified version of the LLP risk algorithm (LLPv2) was utilised for risk calculations (see Appendix 2). This incorporated additional respiratory parameters (i.e. COPD, emphysema, bronchitis and tuberculosis) as well as pneumonia, and also included both pipe and cigar usage within the smoking criteria. (Risk assessment was not carried out for negative responders, as this was not possible from the limited information they supplied). High risk positive responders were contacted with a further questionnaire (Questionnaire 2) (see Appendix 3) to establish eligibility for the RCT (i.e. no exclusion criteria met),PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYWxkd2luPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48

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ADDIN EN.CITE.DATA 52 and sent a detailed Patient Information Booklet: these people were also asked to consent to release of their personal information to the UKLS research team. Non-responders to this second invitation were sent a reminder letter (see Appendix 3).2.3.5UKLS research clinicsIndividuals responding to, and eligible on the basis of, the second questionnaire were invited to one of the recruitment centres (Liverpool Heart and Chest Hospital, or Papworth Hospital, Cambridge). They were shown a DVD outlining the UKLS study ADDIN EN.CITE <EndNote><Cite ExcludeYear="1"><Author>UKLS</Author><RecNum>56</RecNum><DisplayText>(56)</DisplayText><record><rec-number>56</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">56</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>UKLS</author></authors></contributors><titles></titles><dates></dates><urls><related-urls><url> and provided with an opportunity to ask any questions in a discussion group (n=6-8) with the recruitment clinic administrator or on a one-to-one basis with a research nurse. Following informed consent, subjects underwent spirometry (FEV1/FVC ratio), and provided blood, buccal swab, nasal brushings and sputum specimens. Recruits also completed a touch-screen questionnaire; this consisted of follow-up epidemiological and clinical questions, and psychosocial and quality of life questions including the Hospital Anxiety and Depression Scale (HADS) ADDIN EN.CITE <EndNote><Cite><Author>Zigmond</Author><Year>1983</Year><RecNum>57</RecNum><DisplayText>(57)</DisplayText><record><rec-number>57</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">57</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zigmond, A. S.</author><author>Snaith, R. P.</author></authors></contributors><titles><title>The hospital anxiety and depression scale</title><secondary-title>Acta Psychiatr Scand</secondary-title><alt-title>Acta psychiatrica Scandinavica</alt-title></titles><periodical><full-title>Acta Psychiatr Scand</full-title><abbr-1>Acta psychiatrica Scandinavica</abbr-1></periodical><alt-periodical><full-title>Acta Psychiatr Scand</full-title><abbr-1>Acta psychiatrica Scandinavica</abbr-1></alt-periodical><pages>361-70</pages><volume>67</volume><number>6</number><edition>1983/06/01</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Anxiety Disorders/*diagnosis</keyword><keyword>Depressive Disorder/*diagnosis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Interview, Psychological</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Outpatient Clinics, Hospital</keyword><keyword>Outpatients/*psychology</keyword><keyword>Patients/*psychology</keyword><keyword>*Psychiatric Status Rating Scales</keyword><keyword>Psychometrics</keyword></keywords><dates><year>1983</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>0001-690X (Print)&#xD;0001-690X (Linking)</isbn><accession-num>6880820</accession-num><urls><related-urls><url> and the Cancer Worry Scale (CWS-R)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MZXJtYW48L0F1dGhvcj48WWVhcj4xOTkzPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 58, 59 adapted for lung cancer. All smokers (both CT screened and non-screened) were offered smoking cessation advice sheets and a list of local NHS Stop Smoking services.2.3.5.1Collection, storage and archiving of clinical samplesUp to 24ml of blood were taken from each participant at the research clinic visit. Blood samples, buccal swabs, nasal brushings and sputum samples were labelled as detailed in the respective UKLS SOP, and packaged. The packages were collected on a daily basis by courier for delivery to the University of Liverpool Experimental Cancer Medicine Centre (LECMC) Good Clinical Laboratory Practice (GCLP) laboratory, where they were processed and entered into their Laboratory Information Management system (LIMS). A quality control check of these samples has been conducted (see Appendix 6).2.3.5.2Use of UKLS samplesA UKLS Access Committee has been established with external members of UKLS and also a patient representative. This access committee will oversee the release of UKLS data and specimens. The principle underlying release will be based on externally funded projects requesting the use of data/material, which will add considerable information and quality to the UKLS trial data.2.4Randomisation and blindingFollowing attendance at a research clinic, recruits were randomised by computer into the intervention arm (LDCT scan, screen group) or the control arm (usual care, non-screen group) at a ratio of 1:1. Individuals were informed which group they were in within two weeks of randomisation. Due to the nature of the intervention, a blinded trial design was not feasible. 2.4.1Sequence generation, allocation concealment mechanism and implementationThe randomisation method was developed by the UKLS database designers (Artex, Netherlands). Upon upload of subjects to the database, each subject was given a randomly generated unique code, consisting of eight characters (0-9; A-Z). All subjects who had given fully informed signed consent were available for randomisation; a minimum of two subjects from the same site (Liverpool or Papworth) were required in order for randomisation to be implemented. Each time randomisation took place, the computer generated a random shift number, so that the order of characters in each participant’s unique code was shifted by this number. The rearranged codes were then ordered alphanumerically, and split on a 1:1 basis into A (intervention) or B (control) groups. The outcome was thus dependent both on the unique codes of the participants available for randomisation, and the random shift number. Randomisation was carried out once per week by the trial manager or data manager, using a button function within the database. As the process was fully automated, the operator was unable to influence the process. Once participants had been randomised, it was not possible for their allocation to change. A more detailed example of the randomisation sequence is given in Appendix 4.2.5InterventionsSubjects in the intervention group received a thoracic LDCT scan several weeks post-randomisation, either at Liverpool Heart and Chest Hospital, or Papworth Hospital, Cambridge. Full details are given in Chapter 4 (Radiology).2.5.1Follow upAny lesions identified on LDCT-screening were treated as per the planned UKLS care pathway in the study protocolPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYWxkd2luPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48

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ADDIN EN.CITE.DATA 52 (e.g. follow up scan or referral to multidisciplinary team) (see Chapter 4; Figure 12). Health and mortality outcomes of UKLS participants in both study arms will be followed for ten years, via the Office for National Statistics (ONS), the Hospital Episode Statistics (HES) database, and the National Cancer Registration Service. The full protocol for the UKLS study is available online. ADDIN EN.CITE <EndNote><Cite ExcludeYear="1"><Author>Field</Author><RecNum>60</RecNum><DisplayText>(60)</DisplayText><record><rec-number>60</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">60</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>Field, J, HTA protocol</author></authors></contributors><titles></titles><dates></dates><urls><related-urls><url> trial working with primary careWhile the UKLS trial did not use primary care-based recruitment of participants, it included strategies to engage effectively with the General Practitioners (GPs) of participating patients. This took the form of updating the GPs in the PCT areas of the progress of the UKLS trial, with specific information on their own patients. The aim was to inform the GPs as to when their patients had been approached and recruited, and to provide them with CT scan results and any significant outcomes. GPs were also given information about how they could contact the study team with any queries or concerns. 2.6.1Initial contact with General PractitionersThe UKLS Project Management team wrote to the lead GP and the practice manager in the GP practice to inform them that individuals in their practice were being approached regarding the UKLS trial. They were provided with a copy of the 14-page UKLS patient information booklet and a poster to place in their surgery to inform patients that the UKLS trial was recruiting in their area. This enabled the GPs to respond to questions their patients may have concerning the trial.2.6.2Spirometry result Once a participant had been consented at the pilot site recruitment centre and the lung function (FEV1/FVC) had been measured as part of the recruitment process, a letter was sent to the GP with a copy of the Lung Function report, to enable them to act on lung function readings which indicated COPD.2.6.3CT scan results letterCopies of letters sent to the UKLS recruits regarding CT scan results were also sent to their GP, with details on the follow-up procedures.2.6.4Referral to Multi Disciplinary TeamDetails of participants referred to the MDT’s with Category 4 nodules, or participants reported to have significant other finding (SOFs), were reported to the GPs.2.6.5General Practitioner questionnaire on significant other findingsA follow up letter with a short questionnaire was sent to all the GPs at the end of the pilot, requesting the outcome of the SOFs. 2.6.6General Practitioner queriesThe UKLS developed a mechanism for dealing with concerns and feedback from primary care. Like all studies of this kind, patients will often discuss their participation with their GP; furthermore, GPs were informed about the involvement of their patients in the trial. As a consequence, while the trial was conducted largely independently of primary care (in terms of recruitment of patients and follow-up of patients with 'positive' screening results), GPs were made aware of the study and were able to contact the study team with any concerns.We received a small number of written communications from GPs. They were read first by the 'core' study team (led by John Field) and a draft response formulated. Then, the UKLS primary care lead (David Weller) was sent the draft response for further input. There were no complaints of adverse clinical outcomes from the screening process amongst the correspondence received. On the whole complaints related to 1) misunderstandings about who was responsible for follow-up investigations (while the study materials indicated that all such follow-up would be co-ordinated by the study team, in a couple of instances the GP thought he/she was responsible for ordering the investigations) and 2) concerns over patient anxiety generated through involvement in the study (again, through misunderstanding of written materials and communications).In all cases the issues appeared to be addressed and resolved through our letters back to GPs - we did not hear further from the GPs concerned, and there are no ongoing unresolved issues.2.7Outcomes2.7.1Primary outcomesPopulation-based recruitment based on risk-stratification: Compliance rate percentage (response to initial questionnaire); percentage with LLPv2 risk > 5%; percentage response to questionnaire 2 “wishing to participate”Management of trial through web-based database: The UKLS trial recruitment, CT images and clinical pathway were successfully managed through the bespoke web based UKLS database.Optimal characteristics of CT readers (radiologists vs radiographers): radiologists and radiographers were assessed at each pilot site and also at the second read site.IV. Characterisation of CT-detected nodules utilising volumetric analysis: The proportions of the CT screened individuals receiving a repeat CT scan (at 3, 6, 9 or 12 months) are reported.V.Prevalence of lung cancer at baseline: The base line for the UKLS was found to be 1.7%. In addition details are provided for those individuals diagnosed at three month repeat scan)Secondary outcomesVI.Socio-demographic factors affecting participation.VII. Psychosocial measures (cancer distress, anxiety, depression, decision satisfaction): Likely psychosocial impact of participating in a lung cancer screening trial (Hospital Anxiety and Depression Scale; Cancer Worry Scale adapted for lung cancer; Decision Satisfaction). Preliminary data on reasons for non-participation/barriers to participationVIII. Cost-effectiveness modelling: Modelling of Health Economics data regarding the likely consequences of undertaking a larger trial or population screening (using EQ5D questionnaire)2.7.3Tertiary outcomeFuture pooling of pertinent data with similarly designed European lung cancer screening trials, such as NELSON.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 Data to be pooled are those relating to lung cancer detection, incidence and mortality. (The pooling of the data, and subsequent interpretation, lies outside the remit of this report).2.8Sample sizeThe original protocol for the pilot UKLS study allowed for a cohort of approximately 82,000 individuals to be approached, with the aim of identifying and randomising 4,000 high risk individuals from this cohort. Initially, 88,897 individuals were approached, and, although the response rate was as expected, the number of responders at high risk of lung cancer was much lower than anticipated. (Data presented later in this report suggest that this may partly reflect a response bias). The protocol was amended to enable a total of 250,000 individuals to be approached to provide at least 4,000 high risk individuals. The actual number approached (including the original cohort of 88,897) was 247,354; this yielded 4,061 high risk individuals who were enrolled into the RCT. However, a total of 8,729 high risk individuals were identified from the 250,000 approaches.2.9Data storage and analysisData were input to and stored on the bespoke UKLS system builder database, built by Artex (Netherlands), with data security approved by the NIGB. All contacts with participants, letters, CT findings etc. were recorded in the database. Statistical and data analysis was undertaken using Stata v12.2.9.1Socioeconomic data analysisIndex of Multiple Deprivation (IMD) rank ADDIN EN.CITE <EndNote><Cite><Author>DCLG</Author><Year>2010</Year><RecNum>61</RecNum><DisplayText>(61)</DisplayText><record><rec-number>61</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">61</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>DCLG</author></authors></contributors><titles><title>English indices of deprivation 2010</title></titles><dates><year>2010</year></dates><urls><related-urls><url> was obtained from postcode data in an anonymised form for all 249,988 individuals whose details were provided by the PCTs (including non-responders). UK IMD data were analysed and reported as ranks within quintiles based on England-wide population data: Quintile 1 (Q1; most deprived) = IMD ranks 1-6496; Quintile 2 (Q2; above average deprivation) = 6497-12993; Quintile 3 (Q3; average) = 12994-19489; Quintile 4 (Q4; below average deprivation) = 19490-25986; Quintile (Q5; least deprived) = 25987-32482.The English Indices of Deprivation 2010 comprise the official measure of deprivation in England. The most commonly used of these ten indices is the IMD, which is calculated from 38 separate indicators, based on weighted data from the following seven domains: Income Deprivation (22.5%), Employment Deprivation (22.5%), Health Deprivation and Disability (13.5%), Education, Skills and Training Deprivation (13.5%), Barriers to Housing and Services (9.3%), Crime (9.3%), and Living Environment Deprivation (9.3%). For the purposes of measuring deprivation, England is divided into 32,482 ‘Lower layer Super Output Areas’ (LSOAs), with each area being carefully defined by its local geography, and containing in the region of 1,500 residents (minimum 1000 residents). IMD ranks, which provide a relative measure of deprivation in small areas across England, range from 1 (most deprived LSOA) to 32,482 (least deprived LSOA).2.9.2UKLS pilot trial statistical analysis planAs this is a pilot trial, not powered for the endpoint of lung cancer mortality, analysis of the endpoint features is secondary in this plan, as the main UKLS trial was not funded. However, at the appropriate time, lung cancer mortality data will be analysed and will be pooled with the NELSON trial data. Risk scores were analysed based on a modification of the LLP risk model as publishedPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYXNzaWR5PC9BdXRob3I+PFllYXI+MjAwODwvWWVhcj48

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ADDIN EN.CITE.DATA 20, 62 (see Appendix 2). The modifications took account of other smoking materials with the same projected risk as cigarette smoking and other respiratory diseases with the same projected risk as pneumonia.2.9.2.1Primary analysesDescriptive tables of numbers in the pathway to recruitment; number approached, numbers and percentages not responding, responding negatively, responding positively, positive responders deemed eligible, recruited, and randomised to each group. The same figures stratified by age, sex and region.Descriptive tables of recruits to both arms, in terms of demographic variables, individual risk factors, and combined LLPv2 risk score. These will not be accompanied by formal inference, as the randomisation implies that any differences between the two arms will be by chance. ADDIN EN.CITE <EndNote><Cite><Author>S.</Author><Year>2007</Year><RecNum>63</RecNum><DisplayText>(63)</DisplayText><record><rec-number>63</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">63</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Senn S. </author></authors></contributors><titles><title>Statistical Issues in Drug Development.</title><secondary-title>Hoboken: Wiley.</secondary-title></titles><periodical><full-title>Hoboken: Wiley.</full-title></periodical><dates><year>2007</year></dates><urls></urls></record></Cite></EndNote>63Diagnostic cascade in intervention arm. Number recruited, numbers and percentages screened, given further investigation, diagnoses of lung cancer.Numbers (%) receiving specific further investigations: further imaging bronchoscopy, biopsy, surgical resections.Clinical and TN staging of cancers diagnosed as a result of the CT screen in the intervention arm.Estimate associations between recommendation of further investigation and demographic variables, individual risk factors and the LLP risk score, using logistic regression. ADDIN EN.CITE <EndNote><Cite><Author>D.</Author><Year>1993</Year><RecNum>64</RecNum><DisplayText>(64)</DisplayText><record><rec-number>64</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">64</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Clayton D.</author><author>Hills M. </author></authors></contributors><titles><title>Statistical Models in Epidemiology</title><secondary-title>Oxford: Oxford University Press,</secondary-title></titles><periodical><full-title>Oxford: Oxford University Press,</full-title></periodical><dates><year>1993</year></dates><urls></urls></record></Cite></EndNote>64Estimate associations between screen-detected cancer in the intervention arm and demographic variables, individual risk factors, and LLP risk score, using logistic pare lung cancer incidence over two years and stage distributions of lung cancers diagnosed between intervention and control arms, using Poisson regression.Descriptive tables to illustrate results of (f), (g) and (h).2.9.2.2Secondary analysesDetailed analysis of demographic and risk factors by response and recruitment status, using logistic regression, first with response as endpoint and second with recruitment as endpoint.Long-term follow-up of both arms for incidence by stage of lung cancer, to be analysed by proportional hazards regression. ADDIN EN.CITE <EndNote><Cite><Author>D.</Author><Year>2003</Year><RecNum>65</RecNum><DisplayText>(65)</DisplayText><record><rec-number>65</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">65</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Collett D. </author></authors></contributors><titles><title>Modelling Survival Data in Medical Research.</title><secondary-title>London: Chapman and Hall</secondary-title></titles><periodical><full-title>London: Chapman and Hall</full-title></periodical><dates><year>2003</year></dates><urls></urls></record></Cite></EndNote>65Long-term follow-up of both arms for all-cause mortality, to be analysed by proportional hazards regression.Estimate association of lung cancer incidence in follow-up with baseline findings and procedures in the intervention group, using proportional hazards regression.Descriptive tables to illustrate results of (a), (b) and (c), (d).Radiology and pathology reviews will be the subject of separate protocols.CHAPTER 3: RECRUITMENT OF PARTICIPANTS3.1Introduction and rationaleSuccessful introduction of any screening programme requires knowledge of the likely response and uptake, which can potentially be affected by a number of socio-demographic factors. From both an ethical and a cost-effectiveness point of view, it is imperative to target screening towards individuals who are at sufficiently high risk to warrant undergoing a screening intervention. It is also important to identify ‘hard to reach’ groups within the population, so that there is the potential to target these groups specifically. This chapter therefore focuses on the response to the screening invitation, and the socio-demographic factors affecting risk of lung cancer and decision to participate in the trial.3.1.1Objectives and outcomesTo document overall response rate to the initial invitation to participate in UKLS, percentage of positive responders at high risk of lung cancer, and trial participation rate amongst high risk responders. To assess the impact of specific socio-demographic factors (age, sex, socioeconomic status, region and smoking status) on trial uptake. To use these data to suggest particular demographic groups to be targeted to maximise screening uptake in any future lung cancer screening programme.3.2MethodsThe methods for this chapter are described in detail in chapter 2.3.3Results3.3.1Overall response rate and riskFigure 2 and Appendix 5 show the overall response rate, numbers at high risk, and clinic attendance. Details of 249,988 individuals in the 50-75 age range were provided by the PCTs to the data management company. The PCTs sent additional information requesting that 2,634 of these not be contacted (e.g. because they were recently deceased). The remaining 247,354 individuals were therefore approached by the data management company. Out of these, 148,608 (60.1%) were non-responders (no questionnaire returned), 22,788 (9.2%) were negative responders (non-participation questionnaire returned), and 75,958 (30.7%) were positive responders (questionnaire 1 returned; willing to participate). Of the positive responders, 8,729 (11.5%) were classified by the LLPv2 risk prediction model to be at high risk (5% or greater over the next five years) of developing lung cancer (mean LLPv2 risk score = 8.77%, versus 1.00% for the low risk group). Of the high risk responders, 5,967/8,729 (68.4%) returned the second questionnaire and agreed to participate in the RCT. A total of 1,291 individuals were subsequently excluded as either (a) they did not meet the inclusion criteria (e.g. because they had had a previous CT scan of the chest), (b) they had not completed the eligibility questionnaire correctly, (c) they replied after maximum trial recruitment numbers had been reached, or (d) they were unable to give fully informed consent). Five hundred and eighty two individuals either changed their mind or failed to attend clinic, and a further 33 attended the recruitment clinic but declined to consent. In total, therefore, 4,061 individuals (5.3% of all positive responders, and 46.5% of all high risk positive responders) consented and were recruited into the RCT (see Figure 2).Figure 2 Consort Diagram showing participant flow from initial contact to CT screening 34232855515610003.3.2Factors influencing response rate and riskSex, age, socioeconomic status, region (North vs. South) and smoking status were analysed with respect to (a) response to the initial invitation letter, (b) risk status, and (c) participation of high risk people in the UKLS screening trial. The actual numbers involved are tabulated in Appendix 5; trends are discussed in this chapter.3.3.2.1SexMen and women were approached in approximately equal numbers (n=123,748 and 123,606 respectively), but females were slightly more likely to respond positively to the first questionnaire (30.3% of males and 31.1% of females were positive responders). However, men were 2.4 times more likely to be classified as at high LLP risk: 2016 (16.4%) of male and 832 (6.7%) of female positive responders had a high LLP risk score. The frequency of non-responders was greater for men than for women (62.0% of males; 58.2% of females), and the converse was true of negative responders (7.7% of males; 10.7% of females). Having been identified as high risk, and invited to participate in the trial, females were more likely than males to opt out of UKLS (45.5% vs. 35.8%; Chi-squared = 44.987, p<0.0001).3.3.2.2AgePositive response rate ranged from 26.6% in the 50-55 age group to 35.0% in the 61-65 age group, before dropping to 27.6% in the eldest (71-75) age group (see Figure 3). Age is a major component of the LLP risk model, so unsurprisingly had a major impact upon risk classification: only 82 out of 16,273 positive responders (0.5%) in the 50-55 age group were classified as high risk, compared to 2,046 (24.8%) in the 71-75 group. In addition to the youngest (50-55) age group having the lowest positive response rate and the least likelihood of being at high risk, high risk individuals in this age group were also less likely than those in any other age group to consent to participate in the RCT; only 29 out of 82 (35%) consented. Thus, overall in the 50-55 age group, just 29 of 61,168 individuals originally approached (0.05%) were recruited to the RCT (see Figure 4). The different age profiles of the original approached group versus high risk clinic attendees are shown in Figure 4. A total of 94.2% of high risk clinic attendees were aged 61-75 years (compared with 58.1% of the 247,354 subjects originally approached).Figure 3: Impact of age upon initial response rate, LLP risk and trial consent rate When lung cancer risk was considered by individual year of age, there was a steady increase in the percentage of people at high risk, with a progressive increase starting from age 59 (6.1% at high risk; see Figure 5)Figure 4: Age distribution of the original approached population sample, positive responders, high risk responders and recruits to the RCT. In the latter two categories, percentages for the youngest age group are shown to the left of the y-axis. Nearly 95% of trial recruits were aged 61 or over.Figure 5: Percentage of UKLS positive responders (n=75,958) with an LLP risk of >5%, by individual year of age. (There are n=8 individuals aged 76y; these are included with the 75 year old group). See Appendix 5 for primary data.3.3.2.3Socioeconomic statusThe English IMD rank ADDIN EN.CITE <EndNote><Cite><Author>DCLG</Author><Year>2010</Year><RecNum>61</RecNum><DisplayText>(61)</DisplayText><record><rec-number>61</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">61</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>DCLG</author></authors></contributors><titles><title>English indices of deprivation 2010</title></titles><dates><year>2010</year></dates><urls><related-urls><url> was available on 247,328 individuals. Analysis of IMD data showed that the social demographic characteristics of the two recruitment areas (Liverpool and Cambridgeshire) were markedly different. Almost 45% of the Liverpool area residents approached for UKLS fell into the most deprived quintile of the English population, compared with less than 10% in Cambridgeshire. However, as planned in the study design, the total UKLS approached sample was similar in socioeconomic distribution to the entire English population (see Figure 6).Figure 6: Social demographics of UKLS sample. Distribution of individuals within IMD National Quintiles, by area. Much more deprivation is seen in the Liverpool areas (Liverpool, Knowsley and Sefton), than in the Cambridgeshire areas (Cambridgeshire, Peterborough and Bedfordshire). However, the overall socioeconomic distribution of the UKLS sample is similar to that of the entire English population: this was an inherent feature of the study design.The positive response rate increased steadily with higher socioeconomic status: 21.7% of individuals in the lowest (most deprived) IMD quintile gave a positive response compared with 39.7% in the highest quintile (p<0.001;) (see Figure 7). The proportion of individuals with a high LLP risk score decreased with higher socioeconomic status; ranging from 18.2% in the most deprived quintile to 8.3% in the least deprived quintile (p<0.001;) (see Figure 8). As the social gradient of response and the social gradient of LLP risk were offset by each other, the socio-demographic spectrum of the individuals attending clinic was in proportion to that of the original approached sample (see Figure 8). People attending clinic therefore spanned all IMD quintiles in roughly equal numbers, including a representative proportion from more deprived postcodes (see Figure 8). However, of the high risk individuals invited for screening, there was a trend towards individuals of higher socioeconomic status being more likely to consent to participate in the trial (see Figures 7 and 8).Figure 7: Impact of socioeconomic status upon initial response rate (lower line), LLP risk (bars) and trial consent rate (upper line).Figure 8: Socioeconomic distribution of the original approached population sample, positive responders, high risk responders and recruits to the RCT. The social demographic spectrum of the high risk individuals participating in the trial is roughly in proportion to that of the original approached sample.3.3.2.4RegionThere was a higher response rate from individuals in the southern region (Cambridgeshire, Peterborough and Bedfordshire) compared with the northern region (Liverpool, Knowsley and Sefton) (35.0% vs 26.4%). Much, but not all, of this difference could be accounted for by the different social demographic spectrum between the two regions. When stratified by IMD quintile, there was still a tendency for a better response rate from the southern compared to the northern region (see Figure 9). Within each IMD quintile, individuals from the North were slightly more likely than those from the South to be at high risk of lung cancer (see Appendix 5). In the southern region, the likelihood of high risk people consenting to the RCT increased with IMD quintile (from 18% to 60%), whereas, in the northern region, the likelihood of consent was static (around 45%) for the lowest four IMD quintiles, but dropped to 35% in the highest IMD quintile.Figure 9: Positive Response rate to initial approach, stratified by both region (North/Liverpool and South/Cambridgeshire) and IMD Quintile. A slightly better response rate was observed in the Southern region in each IMD quintile.3.3.2.5SmokingOf the 75,958 positive responders, 73,934 (97.3%) gave information about their smoking habits. A total of 43.4% were never-smokers, 14.7% current smokers, and 39.3% ex-smokers (see Figure 10). Of the 22,788 negative responders, 22,024 (96.6%) provided information about their smoking habits. Never smokers totalled 51.3%, 9.1% were current smokers, and 36.2% ex-smokers (see Figure 10). National smoking data (from the Office for National Statistics) for the 50+ age group give an expected distribution of 49.5% never-smokers, 17.5% current smokers, and 33% ex-smokers. ADDIN EN.CITE <EndNote><Cite><Author>Robinson</Author><Year>2009</Year><RecNum>66</RecNum><DisplayText>(66)</DisplayText><record><rec-number>66</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">66</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Robinson, S. Harris, H.</author></authors></contributors><titles><title>Smoking and drinking among adults, 2009: A report on the 2009 General Lifestyle Survey</title></titles><dates><year>2009</year></dates><publisher>Office for National Statistics</publisher><urls><related-urls><url> Thus the observed values in our sample of both positive and negative responders are significantly different from population values (Chi-squared test; p<0.0001). The data suggest that ex-smokers are more likely to respond positively to a screening invitation, with current and never smokers being less likely to respond positively. Negative responders are enriched with never-smokers, but comparatively few current smokers respond negatively. From expected population figures (and accounting for known figures from responders) it was possible to estimate the smoking status of the non-responders, of whom approximately 20% are probably current smokers (see Figure 10).Figure 10: Smoking status amongst UKLS positive and negative responders, and likely smoking status amongst non-responders, based on expected values from age 50+ population figures less known figures from responders. In this case, ‘non-responders’ includes 2,024 positive responders and 764 negative responders who did not provide their smoking status.Unsurprisingly, smokers and ex-smokers were much more likely than never-smokers to have an LLP risk of >5%. Of the 75,958 positive responders, 3,724 (33.5% of 11,130) current smokers and 4,995 (16.7% of 29,855) ex-smokers were designated high risk. However, only ten (0.03% of 32,949) never-smokers had a high LLP risk score, and only two of these consented to participate in the RCT. In total, therefore, 21.3% of current and ex-smoking positive responders were high risk, and 9.9% participated in the RCT. Of the high risk individuals, ex-smokers were more likely than current smokers to consent to participate in the RCT (49.9% vs. 42.1%; Chi-squared = 27.744; p<0.0001).3.3.3Interaction between smoking and socioeconomic statusAs people in more deprived socioeconomic groups are more likely to smoke, it is important to control for any possible confounding effects. Data were therefore stratified by both IMD quintile and smoking status. For all IMD national quintiles, the proportion of ex-smokers among positive responders was around 40%. Of the positive responders in the most deprived quintile, 27.6% were current smokers, decreasing to 9.1% in the least deprived quintile (see Appendix 5). Comparing positive and negative responders, there were proportionally more current smokers among positive responders, and proportionally more never smokers among negative responders, across all IMD quintiles. There were proportionally more ex-smokers in positive responders in all but the most deprived IMD quintile (see Appendix 5).Based on known smoking prevalence rates for 50-75 year olds in each IMD quintile, ADDIN EN.CITE <EndNote><Cite><Author>ESDS</Author><Year>2010</Year><RecNum>67</RecNum><DisplayText>(67)</DisplayText><record><rec-number>67</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">67</key></foreign-keys><ref-type name="Dataset">59</ref-type><contributors><authors><author>ESDS</author></authors><secondary-authors><author>UK Data Archive, Economic and Social Data Service</author></secondary-authors></contributors><titles><title>Health Survey for England, 2010</title></titles><dates><year>2010</year></dates><urls><related-urls><url> it was possible to calculate the expected number of smokers in each IMD quintile for the 247,354 approached subjects (see Appendix 5). By subtracting the known number of smokers amongst positive and negative responders, this allowed an estimation of smoking status for non-responders in each IMD quintile. It was then possible to estimate the percentage of current smokers, ex-smokers and never smokers who respond positively to the first screening invitation (see Figure 11). The calculated response rate is generally lower among current smokers compared to the overall response rate for the relevant quintile (i.e. there is a smaller proportion of smokers amongst responders than would be expected from quintile-matched population figures). However, in the most and least deprived quintiles it appears that the response rate among smokers is similar to, or greater than, the overall response rate for that quintile (see Figure 11). For ex-smokers and never-smokers, positive response rate increases with socioeconomic status across all IMD quintiles, whereas the positive response rate amongst current smokers only rises in the upper two quintiles of IMD. The group least likely to respond positively are never smokers in the most deprived IMD quintile (calculated as 17.2% positive response rate), followed by current smokers in the three most deprived quintiles (calculated as 21.0-22.2% positive response rate). The highest positive response rate is seen in current smokers from the least deprived IMD quintile (calculated as 45.2% positive response rate). The general trend across all IMD quintiles is consistent with the overall trend discussed above: ex-smokers are the most likely group to accept an invitation to be screened for lung cancer.Figure 11: Interaction between smoking status and socioeconomic status in determining initial response to the UKLS invitation. Calculated positive response rate, percent, by smoking status and IMD Quintile. The line (and percentages) denotes the known positive response rate of all individuals within that IMD quintile. Calculations were based on Health Survey for England population smoking figures for each IMD quintile, for an age group of 50-75 years (see Appendix 5).3.4DiscussionThe overall response rate from the 247,354 individuals approached from the population was 30.7%, which is consistent with the response rate anticipated when planning the trial, and the response rate from similar studies. A number of demographic factors were found to influence recruitment, participation and risk status. The main findings are discussed below.3.4.1SexAlthough women are slightly more likely than men to respond positively to an invitation to take part in UKLS, more men than women are designated as being at high LLP risk (6,141 males versus 2,588 females, giving a gender ratio of 2.4:1). However, national lung cancer incidence figures for the 50-74 age group in the UK give a much smaller male : female ratio of 1.34:1. ADDIN EN.CITE <EndNote><Cite><Author>CRUK</Author><Year>2012</Year><RecNum>1</RecNum><DisplayText>(1)</DisplayText><record><rec-number>1</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>CRUK</author></authors></contributors><titles><title>Cancer Stats</title></titles><dates><year>2012</year></dates><urls><related-urls><url> In addition, as high risk women are significantly more likely than high risk men to opt out of the screening trial, it is possible that women are under-represented in the trial. It will be interesting to consider the longer term sex-related outcomes in the UKLS participants in future follow up, as data become available through ONS, cancer registry and HES.3.4.2AgeResponse rate was highest in the middle age group (61-65 years), and lowest in the youngest group (50-55 years). There was a fall-off in response in the oldest age group (71-75 years), who conceivably are less concerned about their risk of lung cancer, or anticipate more practical difficulties with participating in the study. Nevertheless, as the oldest age group has the greatest percentage at high LLP risk (24.8% of 71-75 years positive responders were high risk) this age group was reasonably well represented in terms of trial participation (22.4%, of clinic attendees were aged 71-75 years, compared to 12.1% of the originally approached population). However, as around one third of lung cancers in the UKLS age range occur in individuals of 71-75 years, ADDIN EN.CITE <EndNote><Cite><Author>CRUK</Author><Year>2012</Year><RecNum>1</RecNum><DisplayText>(1)</DisplayText><record><rec-number>1</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>CRUK</author></authors></contributors><titles><title>Cancer Stats</title></titles><dates><year>2012</year></dates><urls><related-urls><url> it could be argued that it would be desirable to screen more people from this oldest group. The best represented age group in the RCT itself was the 66-70y group, who accounted for 39.8% of trial participants.Only 234 (5.8%) clinic attendees were aged 60 or below, of whom only 29 (0.7%) were in the 50-55 years age group. Thus the youngest (50-55 years) age group yielded a trial participation rate of just 0.05% of the 61,168 originally invited to participate in UKLS. This has implications for cost-effectiveness, and, under the LLP model of risk prediction, suggests that it would not be prudent to include this age group in any future population-based lung screening studies or programmes.In order to determine more accurately when response rate and risk increase to a point where screening is viable from a detection and cost-effectiveness point of view, we analysed LLP risk by individual year of age. The results suggest a fairly sharp increase at 59 years of age in the percentage of positive responders at high LLP risk, from around 3.1% at age 58 to 6.1% at age 59 and 7.5% at age 60. This suggests that setting the lower cut-off point for eligibility at 60 years old would be a reasonable strategy for future studies. 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ADDIN EN.CITE.DATA 70 Unlike with other cancers (e.g. breast cancer) there are marked socio-demographic differences in lung cancer risk, with individuals from lower socioeconomic groups being at greater risk of developing the disease: this largely relates to disparate tobacco use in different socioeconomic groups. It is therefore not ideal that those at highest risk are the least likely to take up the offer of screening. Our data suggest that this works at two levels: individuals at highest risk of lung cancer (i.e. from the lower socioeconomic groups) are less likely to respond to the initial screening invitation, and also less likely to attend clinic after having been identified as at high risk. Consideration will have to be given to addressing this in any future screening programme. Within each socioeconomic quintile, although there is a significant difference in screening uptake between Liverpool and Cambridgeshire, the difference is not large. Most of the difference in uptake between the two regions therefore relates to the different social demographics of the two areas.3.4.4Smoking statusAnalysis suggested that ex-smokers are the most responsive to a screening invitation, and the most likely to participate in the trial once identified as high risk. It could be argued that consciously deciding to stop smoking and being motivated to participate in screening are related decisions, perhaps made by individuals who are more health aware, or perceive greater risks from smoking. The initial response rate from current smokers was lower than would be expected based on age matched population figures, possibly suggesting that current smokers are less likely to want to consider their cancer risk, or feel more threatened by the prospect of lung cancer screening. However, it was observed that, if smokers take the trouble to respond, they are more likely in percentage terms to be positive rather than negative responders. The converse is true for never-smokers, who perhaps (correctly) view their own risk as low and hence are over-represented amongst negative responders. Having been identified as at high risk, current smokers were significantly more likely than ex-smokers to opt out of the trial; conceivably this could also be related to their perception of high risk or threat. Only two never-smokers participated in the RCT; it is clear that this is not a group to be targeted in a population-based screening approach.3.4.5Interaction between smoking and Index of Multiple DeprivationThe likelihood of a positive response to a CT screening invitation is lower both in more deprived socioeconomic groups, and in current smokers. As smoking status and socioeconomic status are closely related, it is important to establish whether both independently affect response rate, or whether there is any confound. Predicted population smoking figures stratified by IMD quintile and age-adjusted to the UKLS sample ADDIN EN.CITE <EndNote><Cite><Author>ESDS</Author><Year>2010</Year><RecNum>67</RecNum><DisplayText>(67)</DisplayText><record><rec-number>67</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">67</key></foreign-keys><ref-type name="Dataset">59</ref-type><contributors><authors><author>ESDS</author></authors><secondary-authors><author>UK Data Archive, Economic and Social Data Service</author></secondary-authors></contributors><titles><title>Health Survey for England, 2010</title></titles><dates><year>2010</year></dates><urls><related-urls><url> were therefore compared with the smoking prevalence seen amongst UKLS positive responders in each IMD quintile. This suggests that, in general, socioeconomic deprivation and current smoking status both act independently to lower the positive response rate. However, in the highest and lowest IMD quintiles, the impact of smoking status on response rate is much less marked. In the highest IMD quintile (least deprived), the calculated positive response rate among smokers is higher than the overall positive response rate. In the case of the results from the lowest (most deprived) IMD quintile, the minimal impact of smoking status partially offsets the trend for high risk individuals being less likely to respond. As a general trend across all socioeconomic groups, ex-smokers are the most likely to respond positively (except in the least deprived quintile, where a greater proportion of current smokers respond positively). It is clear that smoking status and socioeconomic status interact when predicting response rate, so in a possible future UK National lung CT screening programme, strategies may therefore need to be devised to target both current smokers and individuals from lower socioeconomic groups.3.5ConclusionThe demographic data from the population approached for the UKLS pilot trial provide a unique insight into the likely response to a lung cancer population screening trial in the UK. This will enable specific recommendations to be made regarding the implementation of any future UK-wide lung LDCT screening programme, such as initiating screening at age 60. Such a programme would need to give due consideration to ways to target those most at risk who may be least likely to take up offers of screening (i.e. the most deprived, current smokers, and the over 70s), and women, who may perceive themselves to be at lower risk. Possible methods would be to incorporate concurrent advertising, and/or utilise modified invitation materials.CHAPTER 4: RADIOLOGICAL INTERVENTIONS AND OUTCOMES4.1IntroductionThis chapter details the radiological methods used in the trial, including CT acquisition, the training and testing of CT readers, the CT reading process and the nodule management strategies used.4.1.1RationaleAccurate and efficient CT reading is crucial to lung cancer screening. In this regard, one of the aims of the UKLS pilot study was to investigate the reliability of various methods of CT reading and CT reading strategies. The method of reading CT scans has potential implications not only for successful detection of lung cancers in a screening programme, but also for the cost-effectiveness of such a programme.4.1.2ObjectivesTo investigate the following:i)The performance of the UKLS thoracic radiologists in nodule detection and interpretation (i.e nodule reporting), having undergone initial training.ii)The performance of UKLS radiographers in nodule reporting, having undergone initial training, and the comparison of radiographer performance with that of radiologists both in the trial and in the wider literature.iii)The performance of radiologists in nodule reporting when using radiographers as part of a “concurrent” reading strategy.4.2Methods4.2.1 CT acquisition, storage and transferCT scans were acquired using a Siemens Definition Flash 128 slice scanner (Siemens, Erlangen, Germany) at Papworth Hospital and Liverpool Heart and Chest Hospital (LHCH). In the initial phase of the study, from November 2011 to December 2011, a Philips (Netherlands) Brilliance 65 scanner was employed at LHCH.Thoracic CT images were obtained (craniocaudally from lung apices to bases) during suspended maximal inspiration, in a single breath hold and without the administration of intravenous contrast. The field of view (FOV) selected was the smallest diameter as measured from the widest point of the outer rib to outer rib (usually no more than 35cm). Thin detector collimation (0.5 – 0.625mm) was used with a pitch of 0.9-1.1. Exposure factors were tailored to patient height and weight, with the aim of ensuring that CT dose index (CTDIvol) was kept below 4 milliGray (mGy), with the effective radiation dose below 2 milliSieverts (mSv). For a 70 kg patient, a KVp (peak Kilovoltage) of 120 was used and a mAs (milliamp.seconds) tailored to achieve a CTDIvol of 1.6mGy. Images were reconstructed at 1mm thickness with 0.7 increment, using a moderate spatial frequency kernel.Thin-section images were transmitted to the following locations: (i) a dedicated local Syngo Siemens (Forcheim, Germany) workstation (so that nodule volumetry could be performed), (ii) local PACS servers (so that images could be stored and subsequently retrieved to the workstation), and (iii) a central site (Royal Brompton Hospital, London) via the NHS Image Exchange Portal (Burnbank Systems, Ipswich, England) for the purposes of double reading.4.2.2Quality assuranceAll local sites were required to have in place daily quality assurance practices for the CT scanner, using a water and body phantom. All doses were recorded at the time of acquisition on the UKLS database by radiographers.During the process of CT reading, any reader (radiologist or radiographer) could highlight defects in the acquisition quality of any CT, such as image degradation due to breathing artefact. All such cases were reviewed and discussed by the local and central radiologist, with respect to diagnostic quality and a decision was made as to whether a repeat CT was required. Additionally, every month for the first six months, and then quarterly, ten randomly selected cases from both sites were reviewed at the central site by two radiographers. CTs were assessed with respect to adequacy of craniocaudal coverage, adequacy of field of view, satisfactory degree of inspiration, correct reconstruction algorithms, presence of motion artefacts, and the recorded radiation dose. Outliers were highlighted to the central site radiologist and feedback was provided to local sites if any of the acquisition parameters were deemed out of range.4.2.3 Reading methodsTo optimise sensitivity and specificity, all baseline CTs were read by two thoracic radiologists at a local (LHCH or Papworth Hospital) and central (RBH) site (see section 4.2.3.5). 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ADDIN EN.CITE.DATA 52 (also see Figure 12): Category 1: Benign nodules fulfilling one of the following criteria: a benign pattern of calcification, fat, measuring less than 3mm in diameter or volume <15mm3. Or: Intrapulmonary lymph nodes fulfilling the following criteria: they lie within 5 mm of the pleura, are <8mm in diameter, are smooth bordered and ovoid and have at least one interlobular septum radiating from their surface.Category 2: If solid and intraparenchymal, a maximum diameter of 3.1-4.9mm or a volume of 15-49mm3. If solid and pleural or juxtapleural, a maximum diameter of 3.1-4.9mm. If non-solid or part solid, a maximum diameter of 3.1-4.9 mm. The solid component has a diameter of <3mm and/or volume of <15mm3. All non-solid/ground glass opacities, independent of diameter.Category 3: If solid and intraparenchymal, a volume of 50-500mm3. If solid and pleural or juxtapleural, a diameter 5-9.9mm. If non-solid or part-solid, a diameter of the ground-glass component of >5mm. If part solid, the solid component has a volume of 15-500 mm3 or has a maximum diameter of 3.0–9.9 mm.Category 4: If solid and intraparenchymal, a volume >500mm3. If solid and pleural or juxtapleural, a diameter of ≥10 mm. If part solid, the solid component has a diameter of ≥10mm or has a volume >500mm3.Nodules were managed as follows: (also see Figure 12)No nodules or Category 1 nodules: No further action required.Category 2 nodules: Follow up CT scan at 12 months.Category 3 nodules: Follow up CT scan at 3 months.Category 4 nodules: Referral to Multidisciplinary Team (MDT).Where follow up scans (at 3 or 12 months) were performed, the volume doubling time (VDT) of the nodule was calculated. VDTs were designated as: < 400 days or ≥400 days.MDT ASSESSMENTCATEGORY 1 Benign nodule or <3mm diameter or 15mm3 or features of an intrapulmonary lymph nodeCATEGORY 2 Solid: volume 15-49mm3 or 3-4.9mm Part solid, solid component <15mm3 or <3mm. Non solid 3mm – 4.9mm .GROWTH VDT <400 days or new solid component of non-solid noduleCATEGORY 4 Solid: volume >500mm3 or>10mmPart solid, solid component: volume >500mm3Follow up CT in 3 monthsCATEGORY 3 Solid: volume 50—500mm3 or 5—9.9mm. Part solid, non-solid component >5mm, solid component:15—500mm3 or3-9.9mm.Non-solid: ≥5mmFollow up CT in 9 months VDT <400 daysVDTshows VDT <400daysNoYesStopLow dose LDCT ScreenAdvanced CancerFollow up CT in 1 yearNoYesGROWTH VDT <400 days or new solid component of non-solid noduleNoYesNodules (single or multiple)MDT ASSESSMENTCATEGORY 1 Benign nodule or <3mm diameter or 15mm3 or features of an intrapulmonary lymph nodeCATEGORY 2 Solid: volume 15-49mm3 or 3-4.9mm Part solid, solid component <15mm3 or <3mm. Non solid 3mm – 4.9mm .GROWTH VDT <400 days or new solid component of non-solid noduleCATEGORY 4 Solid: volume >500mm3 or>10mmPart solid, solid component: volume >500mm3Follow up CT in 3 monthsCATEGORY 3 Solid: volume 50—500mm3 or 5—9.9mm. Part solid, non-solid component >5mm, solid component:15—500mm3 or3-9.9mm.Non-solid: ≥5mmFollow up CT in 9 months VDT <400 daysVDTshows VDT <400daysNoYesStopLow dose LDCT ScreenAdvanced CancerFollow up CT in 1 yearNoYesGROWTH VDT <400 days or new solid component of non-solid noduleNoYesNodules (single or multiple)Figure 12: UKLS Nodule Care Pathway Management ProtocolPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYWxkd2luPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48

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ADDIN EN.CITE.DATA 524.2.3.2 Incidental findingsIncidental findings not related to thoracic malignancy, but regarded by both the central and local reader as likely to be clinically significant, were also highlighted to the participant’s GP. In such cases, a formal report with recommendations for further management was sent from the local site to the GP.4.2.3.3 Nodule volumetry and transfer of nodule data to UKLS databaseAll CTs were read using the “LungCARE” application on the Syngo Siemens workstation, which provides a value for nodule size based on volume. (LungCARE, version Somaris/5 VB 10A, Siemens Medical Solutions). Studies loaded into LungCARE were presented in the following manner: 2 x 2 viewing partition with a default window setting level -500 HU, width 1500 HU, default display of transverse maximum intensity projections (MIPs) at 10mm thickness in cine mode, 1mm-collimation transverse images, 0.7mm-collimation coronal images, and a panel for display of semiautomatic volumetric segmentation analysis if performed. Readers were free to alter MIP thickness and window settings. The reader was required to identify and mark all nodules greater than 3mm diameter or 15mm3. All marked nodules were evaluated for size and recorded in the UKLS database using a non-commercial database electronic soft-copy entry proforma (Artex Nodule Input for UKLS version 4.4, Logiton, Netherlands). Options for nodule categorisation and segment location were available from drop-down menus, while the slice position of the nodule was entered using free-text. Once a reading for a particular nodule had been completed, the information from the proforma was copied and pasted into a structured DICOM report in an Extensible Markup Language (XML) format. The XML file was then transferred to a local network drive, so that the file could be uploaded to the patient’s record on the UKLS database via a PC with web access. The XML file contained information regarding a nodule’s size, table position location, lung and segment location, category and volume.4.2.3.4 Reading of follow-up computed tomography scansA key part of the UKLS protocol was the assessment of volume doubling times to identify significant nodule growth on follow-up CT. If nodules were deemed to have been reliably segmented at baseline and follow up, then volume doubling times were automatically calculated in the UKLS database. For this to happen, CT readers were required to match the data of follow up nodules with baseline nodules, by “dragging and dropping” the data into the same row as the baseline nodule in the UKLS database (see Figure 13). From October 2013, the UKLS protocol was changed such that follow-up CTs were read by a single reader. This was done because the reading of follow-up CTs largely consisted of the task of matching nodules on the follow up scan to existing nodules that had already been identified. (This was in contrast to all baseline scans, where double reading was used because of the theoretical benefits of using two readers to improve lung nodule detection and interpretation).Figure 13: Screenshot from UKLS database. Nodules at baseline, 3 months and 12 months CT are matched in a row. The second figure in each box refers to the slice number and the third figure represents nodule volume. By hovering over the nodule, a figure for VDT is generated automatically. All nodules with a significant VDT < 400 days are highlighted in purple (not shown in this example). 4.2.3.5 Reader selection, training and testing4.2.3.5.1 Training and testing datasetFollowing written agreement from the NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 cases were all pre-anonymised: each study had been given a unique reference number, and no patient identifiable data was visible. Volumetric datasets containing contiguous images were transferred via encrypted hard disk, in Digital Imaging and Communications (DICOM) format to the internal hard drive of the Syngo workstation used at the three participating trial sites (Royal Brompton Hospital, London; Liverpool Heart & Chest Hospital, Liverpool; and Papworth Hospital, Cambridge). The findings of the NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 radiologists’ findings were resolved by consensus opinion or by discussion with a third radiologist from the Royal Brompton Hospital (DMH). In addition, as the NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 study did not specify intrapulmonary lymph nodes, these were also recorded by the UKLS radiologists (AD, AN).4.2.3.5.2 RadiologistsAll radiologist readers were experienced thoracic radiologists at cardiothoracic centres (LHCH, Papworth Hospital or Royal Brompton Hospital), with between six and 25 years experience, who regularly reported thoracic CT as part of daily clinical practice.Twenty CTs from the NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 training dataset, containing examples of all categories of nodules, were selected by the central site radiologist (AD). These cases, and the consensus findings, were shown to local radiologists by AD, and a tutorial on LungCARE was provided if local readers did not have prior experience of using this application. AD received UKLS database training from the database designer (Artex, Netherlands), and in turn AD provided additional tutorials to other readers on uploading nodule data.Prior to commencing formal reading as part of UKLS, all radiologists read a test set of cases (not part of the training set) containing at least 25 nodules, and were required to achieve a detection rate of at least 80% compared to the NELSON radiologists.4.2.3.5.3 RadiographersAll radiographers were from cardiothoracic centres (LHCH, Papworth Hospital and Royal Brompton Hospital) and had experience in thoracic CT acquisition. None had previously reported CTs. The radiographers were first given a PC-based presentation (prepared by AD) covering the basic principles of thoracic CT anatomy. They were then shown 20 CTs containing examples of different types of lung nodules as well as focal opacities such as pleural thickening and atelectasis that can mimic lung nodules. They were provided with the UKLS definitions of lung nodules. The radiographers were also tutored in the use of the LungCARE software on the Syngo workstation (Siemens Medical Solutions, Forcheim, Germany) and the UKLS database.Local site radiographers were trained on a further 80-100 CTs, using a combination of direct supervision by the local radiologist on at least 20 CTs and self-directed learning via the nodule data spreadsheet in the remainder. Radiographers at the Royal Brompton underwent training using a different method. Each radiographer was required to read and record their findings on 100 CTs from the training set, and feedback was provided by a radiologist after every batch of ten cases. The feedback included confirmation of nodules correctly identified, and highlighting missed nodules and overcalls.Finally, prior to commencing reading of UKLS studies, all radiographers read test CTs containing approximately 50 nodules, and were required to achieve an 80% detection rate compared to the NELSON radiologists. 4.2.3.6 Reading strategies testedA) Reading performance of radiologists versus radiographers of UKLS scans. (November 2011 – April 2012).Each CT examination was read by a single radiologist at each of the two participating sites (Radiologist A at Local Site 1 and Radiologist B at Local Site 2). The CTs were then transmitted to the central reading site for a second independent reading (by Radiologist C).The access rights to the UKLS database of the two reading radiologists at the participating sites were configured such that they were not able to view the recordings of other readers, but the central radiologist (Radiologist C) could access these readings because Radiologist C’s role was to identify any discrepant findings that required arbitration. The central radiologist only viewed the readings of the local site radiologists once he had completed his own reading.Four radiographers who were able to commit at least four hours a week over the study period, and who had been trained using the methods described earlier, were selected as readers. Radiographer 1 read CTs at Local Site 1, and Radiographer 2 read CTs at Local Site 2. Two radiographers (Radiographers 3 and 4) read CTs at the central site. Sensitivity and false positives per case were compared using McNemar’s test for nodules identified by radiographers and radiologists for the same CTs. A sub-analysis was conducted to examine the impact of time on radiographers’ and radiologists’ performance (i.e. to examine if there was a learning effect). A further sub-analysis was conducted to identify whether radiographers’ and radiologists’ performance was changed by increasing the size threshold for nodules.After the second reading, the radiologist at the central site (Radiologist C) performed weekly reviews of all nodule candidates identified by radiologists for each subject to identify any discrepancies. Arbitration on discrepancies was provided at the central site by a thoracic radiologist with more than 20 years experience (DMH), and the final consensus view was recorded on the database. B) Reader performance of radiologists reading independently compared to radiologists using radiographers as concurrent readers. (June 2012 – October 2012).The same radiographers and radiologists were used for this study. An additional radiologist (Radiologist D) also participated in reading at the central site. Radiologists either read CTs independently as single readers (as described above) or concurrently with radiographers. The workflow in concurrent reading was as follows:Each CT was first read by a radiographer on the LungCARE workstation, who uploaded his or her report to the UKLS database. The radiographer’s stored nodule recordings (in the form of a DICOM structured report, or DICOM SR) were then made available to the reading radiologist. For each recording, the radiologist had one of three options. He could accept a particular finding if he agreed with it, or reject a finding if he disagreed with it or amend the recording if he agreed that the finding represented a nodule, but disagreed with its categorisation. Then after reviewing the radiographer’s recordings, the radiologist performed an independent search to identify any additional nodules missed by the radiographer. The radiologist then saved his recordings as a new DICOM SR, and uploaded this report to the UKLS database. Reading of CTs between central and local sites was co-ordinated such that either the local site performed concurrent reading and the central site single reading, or vice-versa. This ensured that a comparison between radiologists’ performance using single or concurrent reading could be made for the same CTs.All radiologists were asked to record on the UKLS database the time taken to read each CT and upload the data to the database, to the nearest minute.After both local and central readings had been performed, the radiologists at the central site (Radiologist C or D) reviewed all identified nodules to identify any discrepancies between readers. Arbitration on discrepancies was provided at the central site by a thoracic radiologist with more than 20 years of experience (DMH), and the final consensus answer was recorded on the database. All agreed category 2 to 4 nodules and intrapulmonary lymph nodes were considered positive in the reference standard.Sensitivity, the absolute number of false positive detections (FPs) and average FPs per case (expressed as mean and standard deviation) were separately determined for the cohorts of CTs read independently and concurrently per radiologist. The sensitivity of each radiologist was calculated by dividing the number of true positive nodules detected by the total number of nodules in the reference standard for the cases read by that radiologist. Average FPs per case was calculated by dividing the total number of FPs by the total number of cases read by that radiologist. Differences in proportions were compared using the chi-squared test or the Fisher’s exact test in the case of smaller sample sizes as appropriate. Differences in reading times between concurrent and independent reading for each reader were compared using the independent samples t-test. A post-hoc analysis to determine the correlation between numbers of nodules and reading time was subsequently performed using Spearman’s rank correlation, and differences in correlation coefficients analysed for statistical significance.4.3Results4.3.1CT acquisitionFrom 29th November 2011 to 8th June 2013, baseline CT scans were performed on 1994 participants. Seven of these 1994 scans (0.4%) required repeating (six due to breathing or motion artefact and one due to inadequate craniocaudal coverage).The range of doses for baseline CTs was 0.54 - 3.93 mGy, with a median dose of 1.62mGy.4.3.2Overall performance of radiographers and radiologists Radiographers 1, 2, 3 and 4 had sensitivities of 67.6%, 77.8%, 79.4% and 61.6% respectively (mean sensitivity 71.6 ± 8.5%). Radiologists A, B and C had sensitivities of 88.9%, 87.0% and 74.0% respectively (mean sensitivity 83.3 ± 8.1%). The average false positives (FPs) per case for radiographers 1, 2, 3 and 4 were 1.2 (SD 2.1), 2.9 (SD 2.8), 0.6 (SD 1.0) and 1.1 (SD 1.3) respectively, while that of radiologists A, B and C were 0.5 (SD 0.8), 0.7 (SD 1.0) and 0.2 (SD 0.5) respectively.4.3.3 Direct comparison of radiographer and radiologist performance The sensitivities of each radiographer compared to those of the corresponding radiologists within a particular radiographer-radiologist combination are illustrated in Table 2Table 2 Comparison of radiographer and radiologist sensitivity for the 10 radiographer-radiologist combinations. A negative value for the difference in sensitivity indicates a lower radiologist sensitivity compared to a radiographer. P values are those derived from the McNemar’s test. P values in bold indicate statistically significant results.Radiographer-radiologist combinationNo of CTs readRadiographerSensitivityRadiologistSensitivityDifferencep value1-A 130 67.6 88.0 20.4 0.0008 1-C 130 67.6 74.5 6.9 0.30 2-B 139 77.8 87.4 9.6 <0.0001 2-C 139 77.8 74.5 -3.3 0.20 3-A 68 81.0 92.2 11.2 0.01 3-B 87 78.5 88.2 9.7 0.0087 3-C 155 79.4 76.2 -3.2 0.32 4-A 64 53.8 86.6 32.8 <0.0001 4-B 49 68.7 85.5 16.8 0.0051 4-C 113 61.6 72.0 10.4 0.0119 Radiographer sensitivity was significantly lower than radiologist sensitivity in 7 of 10 radiographer-radiologist combinations (range of difference, 9.7%-32.8%, P < 0.05), and not significantly different in 3/10 combinations. Radiographers had significantly higher average FPs per case than radiologists in 8/10 combinations (range of difference, 0.4-2.6, P < 0.05), and there was no significant difference in the remaining two combinations (see Table 3).Table 3 Comparison of radiographer and radiologist average FPs per case for the 10 radiographer-radiologist combinations. A negative value for the difference indicates a lower radiologist average FPs per case compared to a radiographer. P values are those derived from the paired student’s t-test. P values in bold indicate statistically significant results.Radiographer-radiologist combination Radiographer FP rateRadiologist FP rateDifference P value 1-A 1.1 ± 1.3 0.5 ± 0.8 -0.6 <0.0001 1-C 1.1 ± 1.3 0.1 ± 0.5 -1.0 <0.0001 2-B 2.8 ± 2.8 0.7 ± 1.1 -2.1 <0.0001 2-C 2.8 ± 2.8 0.2 ± 0.5 -2.6 <0.0001 3-A 0.9 ± 1.4 0.5 ± 0.8 -0.4 0.0176 3-B 1.4 ± 2.5 0.6 ± 1.0 -0.8 0.0015 3-C 1.2 ± 2.1 0.1± 0.5 -1.1 <0.0001 4-A 0.4 ± 0.8 0.5 ± 0.8 0.1 0.2009 4-B 0.8 ± 1.2 0.8 ± 1.3 0 0.71 4-C 0.6 ± 1.0 0.2 ± 0.5 -0.4 0.0001 4.3.4Reader performance in the first ten weeks (period 1) versus second ten weeks (period 2)The two radiographers with the lowest overall sensitivity (Radiographers 1 and 4) showed a significant improvement in sensitivity between the first and second 10-week period (sensitivity 50.0% in period 1 versus 74.1% in period 2 for Radiographer 1, 41.8% in period 1 versus 67.2% in period 2 for Radiographer 4, P < 0.005), but their sensitivity in period 2 still did not reach the level of Radiographers 2 and 3, who showed no significant difference in their sensitivity between the two periods. Radiologists’ sensitivity did not significantly differ between the two periods. No radiographer or radiologist demonstrated a significant difference in average FPs per case between the two periods. 4.3.5Comparisons of sensitivity using alternate diameter thresholds There were 236 reference standard nodules that were 5mm or greater in diameter. When considering only these nodules, the number of radiographer-radiologist combinations with significantly lower radiographer sensitivity decreased to 4/10 combinations (range of difference, 17.2%-37.3%, P < 0.05). No significant difference was seen in the remaining 6/10 combinations. 4.3.6Comparison of radiologists’ performance when reporting alone and concurrently with radiographers The overall sensitivity of each radiologist for the different reading methods is detailed in Table 4. The mean sensitivity for radiologists reading independently was 77.5 ± 11.2%, increasing to 90.8 ± 5.6 % with the use of concurrent reading. For all but one radiologist (Radiologist D), statistically significant higher sensitivity was achieved with concurrent reading compared to independent reading.Table 4 Sensitivity of radiologists for each reading method. Except for P values, figures shown are percentages. P values are those derived from the chi-square test. P values in bold indicate statistically significant results.RadiologistReading MethodABCDIndependent 78.9 79.8 62.2 89.2 Concurrent 90.4 98.2 84.5 90.1 Difference 11.5 18.4 22.3 0.9 P value 0.01 <0.0001 <0.0001 0.97 There was a wide variation in the average FPs per case. While the overall mean of average FPs per case increased from 0.33 ± 0.20 with independent reading to 0.60 ± 0.53 with concurrent reading, average FPs per case ranged between 0.06 and 1.38, increasing with concurrent reading for Radiologists A, B and C (and statistically significant for Radiologists B and C), but decreasing for Radiologist D (see Table 5).Table 5 Average FPs per case for each reading method. A negative difference indicates a lower average FP per case with concurrent compared to independent reading. P values are those derived from the independent samples t-test. P values in bold indicate statistically significant resultsRadiologistReading MethodABCDIndependent 0.31 ± 0.75 0.47± 1.10 0.06 ± 0.25 0.48 ± 0.96 Concurrent 0.37± 0.65 1.38 ± 1.46 0.21 ± 0.61 0.42 ± 0.76 Difference 0.06 0.91 0.15 -0.06 P value 0.56 <0.001 0.03 0.69 4.3.7Reading times using independent and concurrent reading methodsThe mean reading times per case for concurrent reading ranged from 6.2 minutes to 8.6 minutes, compared to 7.0 to 12.4 minutes for independent reading (see Table 6). Concurrent reading was faster than independent reading for all radiologists, but this increase in reading speed was not statistically significant for Radiologist C. Furthermore, the maximum decrease in mean reading time was just under 4 minutes (Radiologist A).Table 6 Mean reading times of radiologists for each reading method. Numbers shown are time in minutes, except for P values. Numbers in parentheses are 95% confidence intervals for the mean. A negative difference indicates a shorter time with concurrent compared to independent reading. P values are those derived from the independent samples t-test. P values in bold indicate statistically significant results.RadiologistReading method A B C D Independent 12.4 (11.1,13.5) 8.8 (7.8, 9.8) 7.0 (6.5, 7.5) 8.3 (7.4, 9.3) Concurrent 8.6(7.9, 9.3) 6.2 (5.5, 7.0) 6.9 (6.4, 7.4) 7.0 (6.2, 7.8) Difference -3.8 -2.6 -0.1 -1.3 P value <0.0001 0.0001 0.65 0.03 4.4DiscussionNodule detection for lung cancer screening requires working in a systematic and uninterrupted fashion, with less emphasis on medical/radiological expertise. Systematic CT reading is time-consuming; subsequently entering information into the UKLS database takes more time. We investigated whether radiographers might be more appropriate readers than highly trained radiologists to carry out these tasks.The results of our study show that in seven out of ten pairings, radiographers were less sensitive than radiologists for the detection of lung nodules. Radiographers also generated significantly more false positives than radiologists in eight out of ten combinations. However, the mean (72%) and range (62-79%) of sensitivities of radiographers in this study group compares favourably to figures reported for radiologists in the literature (see Table 7). Reassuringly, radiographers in general did not exceed three average FPs per case, and are thus comparable to computer-aided detection (CAD) systems, where average FPs per case range between 0.3 and 15 per case.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MaTwvQXV0aG9yPjxZZWFyPjIwMDc8L1llYXI+PFJlY051

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ADDIN EN.CITE.DATA 71 Therefore, we believe that these results show that the radiographers in this study (having initially undergone training and testing) may not yet be suitable to act as first and only readers in lung cancer screening. However, their performance compared reasonably well with reported results for radiologists and CAD systems in the literature, and therefore these results certainly do not disqualify radiographers from CT reading in lung cancer screening. Table 7 Sensitivities of radiologists in a selection of nodule detection studies. NR=not reported. Authors Year No. and type of readers Mean detection sensitivityRange Marten et al PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NYXJ0ZW48L0F1dGhvcj48WWVhcj4yMDA0PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 782007 3 radiologists 57 46-68 Current study 2012 3 radiologists 4 radiographers 83 72 74-89 62-79 Instead, radiographers may be well placed to act as a form of reading aid to radiologists in lung cancer screening. We explored this by examining the performance of radiologists when reading alone compared to when reading alongside radiographers (concurrent reading). We found that the sensitivity of radiologists improved for the majority of radiologists (all apart from the most sensitive radiologist) when reading as part of a concurrent reading strategy. This improvement came at the expense of an increase in false positive rates. Such a phenomenon is somewhat difficult to explain, but it may be due to the fact that once an opacity has been highlighted by a radiographer, there is a tendency for radiologists to “overcall” such nodules and a difficulty in dismissing them.Another potential benefit of using radiographers as concurrent readers is in the time saving for radiologists, though this was variable with a time saving of nearly four minutes for radiologist A, and barely any change for radiologist C. Perhaps more importantly, we showed that concurrent reading did not increase reading times. This is relevant, because another (less tangible) benefit of concurrent reading is that it may improve workflow for the radiologist and could potentially reduce reader fatigue. Tasks such as nodule marking, nodule segmentation and data entry are all steps that could be performed by radiographers to assist radiologists, as part of a concurrent reading strategy. An important question, for which the answer is currently unknown in the UK, is whether thoracic radiologists will volunteer to participate in lung cancer screening CT reading. Recruitment could be assisted if it is demonstrated that the reading process can be made efficient by the use of concurrent readers.There are a number of variables that could influence the success of radiographer or concurrent reading strategies:1) In UKLS, all nodules >3mm or 15mmPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Sb29zPC9BdXRob3I+PFllYXI+MjAxMDwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 34, 79 If the threshold for nodule size were to increase in a future lung cancer screening programme such that the total number of nodules recorded per CT reduced, this could have a substantial impact on reading times and performance. In fact, we showed that radiographer performance improved when very small nodules were disregarded from the analysis. Similarly, there is some recent evidence to suggest that a limit may be appropriate to the maximum number of nodules recorded in a single CT because patients with multiple nodules may actually be at lower risk of lung cancer.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NY1dpbGxpYW1zPC9BdXRob3I+PFllYXI+MjAxMzwvWWVh

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ADDIN EN.CITE.DATA 80 In UKLS the maximum number of nodules recorded per CT was 20, but reading could be made more efficient if a lower limit was identified.2) Another important consideration is whether future lung cancer screening programmes use volumetric analysis of nodule size. The benefits (e.g. increased reproducibility of nodule size) and the limitations (e.g. the inability to accurately segment ground glass or perivascular nodules) of currently commercially available volumetry applications are well described ADDIN EN.CITE <EndNote><Cite><Author>Nair</Author><Year>2012</Year><RecNum>82</RecNum><DisplayText>(81)</DisplayText><record><rec-number>82</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">82</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Nair, A.</author><author>Baldwin, D. R.</author><author>Field, J. K.</author><author>Hansell, D. M.</author><author>Devaraj, A.</author></authors></contributors><auth-address>Department of Radiology, Royal Brompton Hospital, Sydney Street, London, UK. arjun7764@</auth-address><titles><title>Measurement methods and algorithms for the management of solid nodules</title><secondary-title>J Thorac Imaging</secondary-title><alt-title>Journal of thoracic imaging</alt-title></titles><periodical><full-title>J Thorac Imaging</full-title><abbr-1>Journal of thoracic imaging</abbr-1></periodical><alt-periodical><full-title>J Thorac Imaging</full-title><abbr-1>Journal of thoracic imaging</abbr-1></alt-periodical><pages>230-9</pages><volume>27</volume><number>4</number><edition>2012/06/15</edition><keywords><keyword>Algorithms</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/*radiography</keyword><keyword>Multiple Pulmonary Nodules/*radiography</keyword><keyword>Observer Variation</keyword><keyword>Solitary Pulmonary Nodule/*radiography</keyword><keyword>*Tomography, X-Ray Computed</keyword><keyword>Tumor Burden</keyword></keywords><dates><year>2012</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>1536-0237 (Electronic)&#xD;0883-5993 (Linking)</isbn><accession-num>22695060</accession-num><work-type>Review</work-type><urls><related-urls><url> ADDIN REFMGR.CITE <Refman><Cite><Author>Nair</Author><Year>2012</Year><RecNum>4877</RecNum><IDText>Measurement methods and algorithms for the management of solid nodules</IDText><MDL Ref_Type="Journal"><Ref_Type>Journal</Ref_Type><Ref_ID>4877</Ref_ID><Title_Primary>Measurement methods and algorithms for the management of solid nodules</Title_Primary><Authors_Primary>Nair,A.</Authors_Primary><Authors_Primary>Baldwin,D.R.</Authors_Primary><Authors_Primary>Field,J.K.</Authors_Primary><Authors_Primary>Hansell,D.M.</Authors_Primary><Authors_Primary>Devaraj,A.</Authors_Primary><Date_Primary>2012/7</Date_Primary><Keywords>Algorithms</Keywords><Keywords>Humans</Keywords><Keywords>Lung</Keywords><Keywords>Lung Neoplasms</Keywords><Keywords>Measurement</Keywords><Keywords>methods</Keywords><Keywords>Multiple Pulmonary Nodules</Keywords><Keywords>Observer Variation</Keywords><Keywords>Radiography</Keywords><Keywords>Radiology</Keywords><Keywords>Solitary Pulmonary Nodule</Keywords><Keywords>Tomography</Keywords><Keywords>Tomography,X-Ray Computed</Keywords><Keywords>Tumor Burden</Keywords><Reprint>Not in File</Reprint><Start_Page>230</Start_Page><End_Page>239</End_Page><Periodical>J Thorac.Imaging</Periodical><Volume>27</Volume><Issue>4</Issue><Address>Department of Radiology, Royal Brompton Hospital, Sydney Street, London, UK. arjun7764@</Address><Web_URL>PM:22695060</Web_URL><ZZ_JournalStdAbbrev><f name="System">J Thorac.Imaging</f></ZZ_JournalStdAbbrev><ZZ_WorkformID>1</ZZ_WorkformID></MDL></Cite></Refman>. However, one advantage that is perhaps less well described is that it allows for a more automated evaluation of nodule size and growth, because volume doubling times can be readily and reliably calculated and growth can be more easily identified. This is important, because we have not shown (and there is no reason to believe) that radiographers would be satisfactorily proficient at identifying growth using visual subjective analysis. Incidentally, this can be a challenge for even experienced radiologists and volumetry also has the potential to assist radiologists in this regard (especially if they are reviewing a large number of nodules as part of a lung cancer screening programme).3) Future studies need to be completed examining the role of radiographers versus CAD as concurrent readers. At present, commercially available CAD systems are substantially limited by the number of false positives that they generate (typically far greater than the results for radiographers in this study).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Hb288L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxSZWNO

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ZT4A

ADDIN EN.CITE.DATA 82 However, if future CAD systems addressed this issue, it may be that CAD software could be used instead of or as well as radiographers.4) We have shown that a substantial proportion of screening CTs identify potentially clinically significant incidental findings (see Chapter 5). Further work needs to be performed to evaluate whether radiographers can successfully recognise incidental findings, especially pneumonia and mediastinal lymphadenopathy, such that the reading process for radiologists could be further streamlined. Whether this is possible may well depend on the types of incidental findings that are deemed sufficiently significant to be highlighted in a lung cancer screening setting.CHAPTER 5: CT scan findings and the early outcome of patients referred to the multidisciplinary team5.1IntroductionThis chapter describes the outcomes of the baseline and follow-up CT scans, and documents any lung cancers that were identified. As all trial participants will be followed up over ten years for health and mortality outcomes, it is only possible here to give an early indication of the outcomes for trial participants. This chapter should therefore not be regarded as giving complete information on the trial participants; only that information available to Autumn 2014. It is therefore not possible to draw any definitive conclusions from the current data regarding the long term effectiveness of lung screening.5.1.1OutcomesThe outcomes were: the number of baseline and follow-up scans performed, categorisation of any lung nodules identified on scan, number of lung cancers diagnosed (including their staging and treatment), and any significant other findings. Mortality is not reported, as data are not mature.5.2MethodsDetailed methods regarding technical aspects of CT scanning/reading are given in the previous chapter. In brief, the trial participants who had been randomised to the intervention arm were given an appointment to attend for a single thoracic CT scan at either Liverpool Heart and Chest Hospital (LHCH), or Papworth Hospital, Cambridgeshire. Scans were double-read by a radiologist at the local site (LHCH or Papworth), and a radiologist at the central site (Royal Brompton Hospital). Where necessary, arbitration was performed by an expert radiologist at the central site.Any lung nodules identified on CT scan were classified into four categories. Full details of these categories and the formal care pathway are shown in the previous chapter (see Figure 12). In summary, nodule categorisation and subsequent care was as follows:No nodules or Category 1 (benign) nodules: no further action required.Category 2 (small, probably benign) nodules: follow up CT scan at 12 months.Category 3 (larger, potentially malignant) nodules: follow up CT scan at 3 months and 12 months.Category 4 (higher chance of malignancy) nodules: immediate referral to Multidisciplinary Team (MDT).For Category 2 or 3 nodules, the volume doubling time (VDT) of the nodule was calculated from the follow up scan (at 3 or 12 months). The VDT was used to assess the likelihood of malignancy. The UKLS trial protocol specified clear criteria for referral to the local lung cancer MDT. These were designed to select those at sufficiently high risk of lung cancer to justify further investigation. The subjects referred included those with a high risk of malignancy at baseline CT (category 4 nodules regarded as suspicious for lung cancer) and those of categories 2 and 3 with nodules that displayed a VDT of <400 days. In addition, the protocol specified action for ‘significant other findings’ (SOF). These included abnormalities detected that required further action, either urgent, such as the finding of a cancer from another primary site or less urgent, such as a likely pneumonia, requiring further imaging follow up or treatment.CT readers were required to make recommendations as per protocol wherever possible. However, radiologists were also at liberty to vary their recommendations on clinical grounds, if, in the opinion of both the local and central readers and local MDT chest physician, the CT appearances were not adequately covered by the protocol.Details of subjects requiring follow up for any of the criteria set out in the protocol were recorded in the UKLS database by the local investigators and from the general practitioners’ records. For lung cancer, stage, pathology and treatment offered were recorded.5.2.1Quality control for pathology of resected nodulesQuality control (QC) for histopathological examination of resected involved exchange between the reference thoracic pathologists at Liverpool (Professor John Gosney) and Papworth (Dr Doris Rassl) of a representative H&E-stained section from all cases. This was accompanied, where necessary, by any immunolabelled sections used in diagnosis and/or classification of the lesion. Sections were reviewed ‘blind’ and responses exchanged with appropriate discussion in cases of discordance.5.2.2Statistical AnalysisLogistic Regression was utilised in order to determine which factors were associated with requirement for further scans or investigations. Stata v12 was used for analysis. The following variables (data obtained from Questionnaire 1) were used in both univariate and multivariate analyses. For analyses including smoking status, the two never-smokers in the CT screening arm of the trial were removed from the analysis.5.2.2.1Variables used in Logistic RegressionRegion (North or South), sex, age group (five year bands), socioeconomic status (IMD), smoking status and duration, history of respiratory disease, history of mesothelioma, history of previous lung cancer, history of asbestos exposure, family history of cancer.5.3ResultsA total of 4,061 high risk individuals consented to take part in the RCT. Six of these were not randomised, either because they had undergone a recent thoracic CT scan (so were not eligible; n=3), changed their mind about participating (n=1), or had no randomisation partner at the same hospital (n=2) The remaining 4,055 high risk individuals were randomised into two groups. Following randomisation, 2,028 individuals were assigned to the screening arm, and offered a thoracic CT scan. Thirty four of these individuals were not scanned. The most common reasons for this were that they withdrew following consent, or a scan was felt to be inappropriate due to a change in their health. 1,994 individuals therefore underwent a baseline CT scan (see Figure 2 in Chapter 3). Details of individuals in both arms of the trial are shown in Table 8; the characteristics of people in both trial arms were very similar with respect to all factors considered.Table 8: Demographic, risk and medical characteristics of n=4,055 individuals randomised to the UKLS intervention (CT screen) and control (non-screen) trial armsTotal n=4055, as n=6 not randomised?Screen Arm (n=2,028)Control Arm (n=2,027)Male: Female Ratio1,529:499 (3.06:1)1,507:520 (2.90:1)North:South Ratio1,023:1,005 (1.02:1)1,023:1,004 (1.02:1)Mean (SD) Age, years67.1 (4.1)66.9 (4.1)Median age, years6767Median IMD Rank17,37417,704Mean (SD) LLPv2 score8.87 (5.12)8.83 (4.71)Median LLPv2 score7.117.35Never smokers, n (%)2 (0.1%)0 (0%)Current smokers, n (%)777 (38.3%)791 (39.0%)Ex-smokers, n (%)1,249 (61.6%)1,236 (61.0%)Smoking duration 10-19 years, n (%) ?117 (5.8%)116 (5.7%)? All smoking duration figures refer to current- and ex-smokers combinedSmoking duration 20+ years, n (%) ?1,895 (93.4%)1,907 (94.1%)Smoking duration unknown, n (%) ?14 (0.7%)4 (0.2%)Asbestos exposed, n (%)763 (37.6%)763 (37.6%)History of respiratory disease, n (%)*1,056 (52.1%)1,023 (50.5%)*asthma, bronchitis, TB, pneumonia, COPD, or emphysemaHistory of blood cancer, n (%)**26 (1.28%)31 (1.53%)**leukaemia or lymphoma, including Hodgkin'sHistory of solid tumour***378 (18.6%)396 (19.5%)***cancers of brain, head & neck, oesophagus, breast, colon or "other"Family history of lung cancer, at any age, n (%)498 (24.6%)554 (27.3%)Family history of lung cancer <60y, n (%)215 (10.6%)215 (10.6%)Family history of lung cancer >60y, n (%)283 (14.0%)339 (16.7%)Family history of other cancer (not lung), n (%)****1026 (50.6%)1019 (50.3%)****cancers of brain, head & neck, oesophagus, breast, colon or "other"Figure 14 shows the outcome of the initial LDCT in a total of 1994 subjects. There were 979 (49.1%) subjects with either clear scans or category 1 nodules that were considered benign or had a probability of malignancy too low to justify further investigation and follow up, as per protocol.A total of 1,015 (50.9%) had category 2 to 4 nodules. There were 479 (24%) subjects who had category 2 nodules, and were therefore recommended for a 12 month repeat scan (see Figure 14). Of these, 474 subjects’ repeat scans showed clear CTs (no significant growth; volume doubling time >400 days), and the remaining five subjects (0.8%) were referred to the MDT. One of these subjects was diagnosed with cancer (which was resected), and four were discharged or are under follow up with a presumed likely benign lesion.-660404754245One of these individuals had a third 3-month follow up scan before referral to MDTEOS = End Of ScreeningOne person discharged from Cat 4 MDT at baseline also had several Cat 3 nodules – so was followed up as Cat 3, with 3 month and 12 month repeat scans, before final discharge. This person is not included in the Cat 3 figures.?00One of these individuals had a third 3-month follow up scan before referral to MDTEOS = End Of ScreeningOne person discharged from Cat 4 MDT at baseline also had several Cat 3 nodules – so was followed up as Cat 3, with 3 month and 12 month repeat scans, before final discharge. This person is not included in the Cat 3 figures.?6560185329311000672909531083250043243502769870005067300276987000437515028778202?002?5242560295973524002448075852599690190019Figure 14: Diagnostic tree showing outcomes of 1994 baseline scansFour hundred and seventy two subjects (23.6%) had Category 3 nodules and therefore underwent a three month interval CT. Following the three month repeat scan, 53 (11.2%) subjects were discharged and 19 (4.0%) were referred to the MDT (two of these had cancer). Nineteen subjects underwent a further three month interval CT. This was part of a protocol amendment that was introduced to account for new nodules appearing on the three month interval CT; the concern was that potentially fast growing cancers might be missed. Following the second three month interval CT, three subjects were discharged, one subject was referred to the MDT, and one subject underwent a further three month repeat scan before referral to the MDT; neither of these had cancer. The remainder comprised 14 subjects who underwent a six month CT (to complete a 12 month interval from the first CT). All of these CTs have been completed with no malignancy detected. Following the first three month interval CT scan, 381 (80.7%) subjects were protocolled for a further nine month interval CT; this resulted in 357 being discharged with no malignancy detected, and 24 being referred to the MDT, five of whom had cancer. Thus a total of 45 individuals initially classed as Category 3 (9.5%) were referred to the MDT. Of these, 11 were thought to have cancer and 34 were discharged or are under surveillance for a likely benign diagnosis. One subject (with metastatic cancer) had chemotherapy, and ten had surgery. Altogether, 10/50 (20%) of subjects referred to the MDT (who had category 2 or 3 nodules at baseline) had confirmed lung cancer.Sixty four subjects were referred to the MDT immediately following the baseline scan (see Figure 14). Thirty one of these were discharged or are under MDT surveillance for nodules with a presumed benign diagnosis, and 33 were thought to have cancer and underwent further investigations. Five subjects had chemo-radiotherapy and 28 surgery, of whom 27 had histopathologically confirmed cancer. Thus 32/64 subjects (50%) of subjects referred to the MDT with category 4 findings at baseline had confirmed lung cancer. Table 9 summarises the number of screened individuals assigned to each category, and the numbers referred to the MDT and identified cancers in each category. Table 9: Overall numbers of individuals in each nodule category, numbers referred to the multidisciplinary team (MDT) and number of lung cancers diagnosedNodule category (management)Cat 1 (discharged)Cat 2 (repeat scan at 12 months)Cat 3 (repeat scan at 3 months then 12 months)Cat 4 (Immediate MDT referral)TOTALNumber in category979479472641994Number referred to MDT0 (N/A)54564114Number of confirmed Lung Cancers0 (N/A)193242Thus, 1952/1994 (97.9%) participants completed screening in the trial with no cancer found. Forty two (2.1%) individuals were diagnosed with lung cancer.5.3.1Diagnostic workup and false positivesIn the UKLS, we defined false positives or rate of recall as those requiring further diagnostic investigation more immediately than a repeat annual screen, but who transpired on such investigation not to have lung cancer. Overall, 951/1994 (47.7%) of subjects underwent at least one further CT after the initial screen. This comprised 479 subjects in Category 2 who underwent a 12 month interval CT and 472 in Category 3 (see Table 10). In addition, at the time of reporting, a further 414 CTs have been carried out as per protocol (see Figure 14). It should be noted that a repeat CT at three months for category 3 nodules was mandated by the protocol. There were a total of 536 subjects (i.e. 472 category 3, and 64 category 4) with nodules requiring a repeat scan. Of the 64 category 4 individuals, 41 were found to have lung cancer.Due to our failsafe policy reflecting the single screen design (i.e. follow up of Category 2 individuals), there were a further 479 individuals for whom a repeat screen was recommended at 12 months, only one of whom was found to have a confirmed cancer.The referral rate to the MDT was low and there were relatively few people with benign disease who had invasive tests. One hundred and fourteen (5.7%) were referred to the MDT of whom 72 (3.6% of the total; 72/1994) did not have cancer. For complete clarity, the proportion of false positive tests is now provided in two ways, which allows an appreciation, in a patient-centred approach, of the variable impact on the subject in a trial or the patient in a programme. A “false positive” that mandates referral to the lung cancer MDT will usually be associated with significant psychological distress, and additional more or less invasive investigations with, in some cases, definitive treatment. An individual with a false positive so defined is thus more likely to suffer harm than one defined in a different way; that is, those subjects who are recalled solely for further CT imaging to clarify the nature of a nodule. The latter is best termed “Interval Imaging Rate” and may, in screening programmes, merely mean continuing in the programme rather than referral to the MDT. For this reason all category 3 lesions without cancer are reported separately as false positives warranting interval imaging. Category 2 findings are not classified as false positives warranting recall as the cancer rate was found to be so low in this study that interval imaging would not be recommended. Thus on examining the number of UKLS participants referred to the MDT clinic, the false positive rate is 3.6% (114-42/1994 =3.6); whilst the Interval Imaging Rate for the Category 3 nodules is 23.2% (472-9/1994).In total, 114/1994 (5·7%) participants were referred to the MDT, of whom 42 (2·1% of all screened) had lung cancer. 5.3.2Details of cancer diagnosesOf the 42 cancers with a confirmed diagnosis, 27 were diagnosed at Liverpool Heart and Chest Hospital, and 15 at Papworth Hospital. Twenty five were Adenocarcinoma, 12 were Squamous Cell Carcinoma, three were Small Cell Carcinoma, one was a Typical Carcinoid, and one was of unknown type (this latter patient was treated with palliation alone). Pathological staging of cancers was completed for 35/42 cancers: 17 were T1aN0 (one Nx), six were TlbN0, two were T2aN0, two were T2bN0, two were T1aN1, three were T2aN1, two were T1-2N2, one was T3N0. Clinical staging was recorded for a further seven cancers (see Table 10). Thus, 67% of the screen-detected cancers that have so far been staged (28/42) were identified at stage 1. Details of all individuals referred to the MDT with a cancer diagnoses are shown in Table 10.Table 10: Details of cancers diagnosed. UKLS case No Baseline Nodule Category*SexAgeTNMFinal STAGEDiagnosisTreatment14M59pT1a pN0IAAdenocarcinomaSurgery24M66pT1a pN0IAAdenocarcinomaSurgery34M66pT1a pN0IAAdenocarcinomaSurgery 44M55pT1b pN0IAAdenocarcinomaSurgery54M63pT1a pN0IAAdenocarcinomaSurgery 64F64pT1a pN0IAAdenocarcinomaSurgery74M67pT1b pN0IASmall Cell CarcinomaSurgery/Chemotherapy 84M62pT1a pN0IASquamous Cell CarcinomaSurgery94M68pT1b pN0IASquamous Cell CarcinomaSurgery104M67pT1a pN0IASquamous Cell CarcinomaSurgery114M73pT1b pN0IASquamous Cell CarcinomaSurgery 124M71pT1a pN0IAAdenocarcinomaSurgery 134M72cT1b cN0 cM0IAAdenocarcinomaRadiotherapy 144M64pT1b pN0IAAdenocarcinoma Surgery 154M68pT1a pN0IAAdenocarcinoma Surgery 164M74cT1a cN0 cM0IABronchogenic CarcinomaPalliative174M69pT1a pN0IASquamous Cell CarcinomaSurgery 184M70pT2a pN0IBAdenocarcinomaSurgery194M67pT2a pN0IBAdenocarcinomaSurgery 204M68pT2a pN1IIASquamous Cell CarcinomaSurgery/Chemotherapy214F67pT1a pN1IIASquamous Cell CarcinomaSurgery/Chemotherapy 224F64pT2b pN0IIAAdenocarcinomaSurgery/Chemotherapy234F73pT2a pN1IIASmall Cell CarcinomaSurgery/Chemotherapy244M63pT1a pN1IIAAdenocarcinomaSurgery/Chemotherapy 254M75pT2a pN1IIASquamous Cell CarcinomaSurgery/Chemotherapy 264M64pT2b pN0IIACarcinoidSurgery 274M68pT1a pN2IIIAAdenocarcinomaSurgery284F69pT1b pN2IIIAAdenocarcinoma Surgery/Chemotherapy 294M63cT1a cN2 cM0IIIASmall Cell Carcinoma Chemotherapy304F60pT3 pN0IIBSquamous Cell CarcinomaSurgery/Radiotherapy314M66cT4 cN3 cM1bIVAdenocarcinomaChemotherapy/Radiotherapy324M64cT3 cN2 cM1bIVSquamous Cell CarcinomaPalliative333 M68pT1a pN0IAAdenocarcinoma (two primariesSurgery343 M69pT1a pN0IAAdenocarcinomaSurgery353M61pT1a pN0IASquamous Cell CarcinomaSurgery 363 M70pT1a pN0IAAdenocarcinomaSurgery 373 F70pT1aNx**IAAdenocarcinoma Surgery383 M66pT1b pN0IASquamous Cell CarcinomaSurgery393 F69cT1a cN0 cM0IAAdenocarcinomaRadiotherapy403M71pT1a pN0IAAdenocarcinomaSurgery413 F75cT4 cN2 cM1bIVAdenocarcinomaSurgery (non pulmonary)Radiotherapy/Chemotherapy422F66pT1a pN0IAAdenocarcinomaSurgeryTable 10 Legend*Baseline Nodule Category – category 4 referred to MDT at baseline, category 3 referred for repeat CT at 3 months and 12 months, category 2 referred for 12 month repeat CT.**Participant underwent wedge resection. Clinical stage was cT1a cN0 cM0.5.3.2.1 Demographics of people diagnosed with lung cancerOf the 42 individuals with a screen-detected lung cancer, 32 were men and ten were women (i.e. 1.8% of all individuals screened, for both sexes). The mean age at trial recruitment for the cancer patients was 66.9y (median 67y; range 55-75 years). This was similar to the mean age of all screened (67.1y). 17/42 (40.4%) people with a confirmed cancer were from the most deprived IMD quintile (Q1), 5 (11.9%) were from Q2, 9 (21.43%) from Q3, 4 (9.5%) from Q4 and 7 (16.7%) from the least deprived IMD quintile (Q5).5.3.2.2 Predictors of subjects requiring further investigationsVariables from Questionnaire 1 (see Appendix 3) were used in both univariate and multivariate logistic regression analyses to establish which factors were associated with participants requiring follow up scans or investigations (i.e. Category 2, 3 and 4 individuals; n=1,015) versus individuals with clear scans (Category 1; n=979). In the univariate analysis, both female gender (Odds Ratio 1.25; 95%CI 1.02-1.53; p<0.032) and a previous history of respiratory disease (Odds Ratio 1.25; 95%CI 1.05-1.49; p<0.012) were associated with requiring further scans. In the multivariate analysis, a previous history of respiratory disease (Odds Ratio 1.28; 95%CI 1.06-1.54; p<0.009) was associated with requiring further scans.5.3.2.3 Further work up by MDTTable 11 shows the further investigations and procedures that have so far been done for subjects referred to the MDT. Only 8.6% of subjects who did not have confirmed cancer had needle biopsies and 4.3% had surgery. These are the two procedures most associated with complications. Overall, of 114 subjects referred to the MDT, 11 subjects (15.3%) without confirmed cancer had either a biopsy or surgery and nobody had both. This includes one subject with likely lung cancer. Thus only 0.55% of subjects screened underwent semi-invasive or invasive tests for benign disease.Table 11 Work up by MDTNo cancer confirmed, n = 72Lung cancer confirmed, n = 42InvestigationNumber%Number%Further CT6184.742100.0Needle biopsy79.71535.7EBUS11.438.3PET1318.053788.9Oncology referral11.41336.1Surgical referral45.53583.3Discharged34.32158.3Other outcome22.9411.1Outpatient follow up2130.02158.3Not suitable for MDT00.0005.3.2.4 TreatmentTable 10 shows the treatment received by subjects with lung cancer. Reflecting the early stage, 35 of 42 subjects (83.3%) had surgery as their primary treatment. Of these eight also had chemotherapy and one had radiotherapy. Chemotherapy was the primary treatment in three patients and radiotherapy was offered to two subjects who declined surgery. Treatment was palliative (supportive care) in two subjects.5.3.3Incidental FindingsTo date, over the course of the UKLS screening trial, 128 Significant Other Findings (SOF) have been identified on the CT scans by the trial radiologists. Most of these findings related to thoracic disease, but 17 extra-thoracic SOFs were also identified. Individuals with a SOF on scan were referred for further investigations and treatment to either a relevant specialist or MDT, or their own GP. Details of the thoracic and extra-thoracic SOFs are shown in Tables 12 and 13 respectively.Table 12: Clinically significant thoracic incidental findings, for which a supplementary radiology report was sent to the GP or appropriate MDTCT FindingN=Aortic dilatation4Severe aortic valve calcification5Mediastinal Mass4Mediastinal or hilar lymphadenopathy6Pneumonia41Bronchiectasis5Pleural thickening8Smoking related interstitial lung disease7Severe emphysema9Interstitial fibrosing lung disease (unspecified)6Non-specific interstitial pneumonia (NSIP)2Usual interstitial pneumonia (UIP)12Sarcoidosis1Oesophageal thickening or dilatation2Breast mass1Lobar collapse2TOTAL115Table 13: Clinically significant extra-thoracic incidental findings, for which a supplementary radiology report was sent to the GP or appropriate MDTCT FindingN=Biliary dilatation1Adrenal mass3Cirrhosis1Hydronephrosis1Liver mass1Pancreatic cysts1Renal mass3Splenomegaly1Thyroid mass1TOTAL13SOFs were managed locally in Liverpool or Papworth. For any incidental findings that were identified before 9th December 2013, the participants’ GPs were contacted by UKLS and asked to provide follow up and outcome data. The results from both Liverpool and Papworth are detailed below. Follow up information for incidental findings arising after 9th December 2013 has not yet been requested, hence the figures below do not include all of the incidental findings detailed above (see Tables 12 and 13).In the Liverpool cohort, 55 SOFs were identified, and the patients’ primary care physician informed. Seventeen different conditions were identified, including eight abdominal conditions. Thoracic conditions included: pneumonia (n=18), pulmonary fibrosis (n=8), pleural thickening (n=5), bronchiectasis (n=3), lymphadenopathy (n=3), severe emphysema (n=3), lobar collapse (n=2), oesophageal thickening (n=2), mediastinal mass (n=2), thyroid mass (n=1), thoracic aortic aneurysm (n=1). Abdominal abnormalities included: adrenal lesion (n=2), abdominal aortic aneurysm (n=2), cirrhosis of the liver (n=1), splenomegaly (n=1), renal mass (n=1), pancreatic cyst (n=1). Further information on these patients was identified from secondary care records and 31 responses from primary care for follow up data. Whilst the vast majority of pneumonias were managed in primary care, at least 22 of the others were referred to secondary and tertiary care for further investigation and management. There were 48 SOFs in the Papworth cohort. The general practitioners of patients with SOFs were contacted by letter and asked to take appropriate action. For five cases with a potentially life threatening condition the letter was followed up with a telephone call a few days later to ensure that action had been taken. Thoracic conditions included: interstitial lung disease (n=10); severe emphysema (n=2); pneumonia (n=16); atypical pneumonia (n=4); lymphadenopathy (n=3); posterior mediastinal mass (duplication cyst) (n=1); thymoma (n=1); aortic calcification (n=4). Abdominal conditions included: renal mass (n=2); adrenal mass (n=1); hepatic mass (n=1); abdominal aortic aneurysm (n=1); biliary dilatation (n=1); hydronephrosis (n=1). In 29 cases, outcomes are known from secondary care records or via the primary care follow-up questionnaire. Of these, the UKLS identified diagnosis was already known to the GP in three cases and no further action was taken. In 12 cases the GP treated the patient and in 14 cases the patient was referred to secondary or tertiary care.Overall, about 5% of patients had a SOF identified. Nearly all of these were previously undiagnosed conditions. Their identification empowered primary care physicians to manage or appropriately refer on, for the patient’s benefit.5.4DiscussionThese preliminary findings from the single screen UKLS study have confirmed several suppositions and hypotheses: The lung cancer detection rate is 2.1%. This means that the LLP risk assessment model has performed as expected. All subjects had to have a five year risk of lung cancer of at least 5%. A greater than 1% risk on prevalence screening would therefore be expected. Unlike other CT screening studies, LLP avoids the problem of screening people at very low risk where the harms of screening may outweigh the benefit. The low threshold for imaging follow up of abnormal findings resulted in 47.7% of subjects needing follow up CT at three months and/or one year. This was to be expected as the UKLS protocol indicated repeat scans for people with Category 2 nodules which accounted for around 50% of nodules. However, only one lung cancer was detected from this category. Nine lung cancers have been confirmed from the group of Category 3 nodules. For both of these categories the probability of finding lung cancer in the nodules is below the baseline risk of the population as a whole and for category 3 (although numbers are too small to draw any real conclusion) it is 1.9%. Two lung cancers were identified by the three month interval CT so it could be argued that this step is necessary. However we are not able to comment on the effect on prognosis of detection of these lung cancers after three months rather than after a 12 month interval. Whether an annual screen is the best strategy for managing both Category 2 and 3 nodules should be determined by reference to other studies with larger numbers and by monitoring outcomes of future screening programmes. The immediate work up strategy for the category 4 findings resulted in the greatest number of confirmed lung cancers; 50% of category 4 subjects referred to the MDT had lung cancer. Overall, only 5.7% of subjects were referred to the MDT, which demonstrates the utility of the imaging recall protocol. Furthermore those subjects who underwent further investigation mostly underwent further imaging with few having minimally invasive or invasive procedures for benign disease. This rate of investigation of benign disease was similar to that found in NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 and NLST. Active treatment rates were high; 83% of subjects with confirmed lung cancer had surgery, reflecting the high proportion of patients with stage 1 or 2 disease. Of those patients who had surgery for later stage 3a disease, this was only detected at surgery, a feature thought to be associated with a more favourable prognosis. A variety of significant other findings were detected. These included the detection of other cancers including malignant melanoma and renal carcinoma as well as important benign conditions such as severe emphysema. Not all of these benign diagnoses were known to the subject or their doctor. The detection of these other conditions (especially cancer) may clearly benefit subjects although it is not possible to measure the effect in this study. It is important to note how many more cancers appeared to be detected in the lower socioeconomic groups. Although numbers are relatively small there was a marked difference between the socioeconomic profile of the subjects screened (which was similar to the population as a whole) and that of those who had lung cancer. This raises a number of issues that may be important if screening programmes are to begin. Since the risk of lung cancer is most strongly related to age and smoking, it is likely that the main reason for the observed rates in the lower socioeconomic groups is that the subjects smoked more. This would be in keeping with the way the LLPv2 selects individuals at a risk of at least 5% over five years. There will be a proportion of people at much greater risk by virtue of their smoking habit. This observation also has implications for the recruitment of people into programmes. At selection, we observed that the lower socioeconomic groups more at risk yet were less likely to participate and we now know that the same group, when recruited, do indeed have a greater prevalence of lung cancer. This serves to emphasise the importance of effective methods of recruitment of these relatively disadvantaged people into screening programmes if the effectiveness of such programmes is to be maximised.CHAPTER 6: PSYCHOSOCIAL OUTCOMES6.1Background6.1.1The importance of assessing psychosocial impact of lung cancer screeningAlongside clinical evidence, an evaluation of psychosocial harms and benefits is essential when considering the introduction of a new screening programme. Studies of cancer screening in the general population have highlighted adverse psychosocial effects, in particular those associated with abnormal, false positive or inconclusive test results.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NY0NhZmZlcnk8L0F1dGhvcj48WWVhcj4yMDA0PC9ZZWFy

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ADDIN EN.CITE.DATA 85-91 Overall, evidence from these trials suggests that LDCT screening does not produce long-term anxiety or other adverse effects which could deter high-risk individuals from further help-seeking or future participation in a lung screening programme. However, it is important to examine psychosocial effects within the UKLS trial context to ensure that any future lung screening programme is tailored to the specific needs of the UK high risk population. 6.1.2Barriers to uptake of lung cancer screeningPrevious studies have highlighted some of the demographic and psychosocial barriers to lung screening uptake. Silvestri et al ADDIN EN.CITE <EndNote><Cite><Author>Silvestri</Author><Year>2007</Year><RecNum>93</RecNum><DisplayText>(92)</DisplayText><record><rec-number>93</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">93</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Silvestri, G. A.</author><author>Nietert, P. J.</author><author>Zoller, J.</author><author>Carter, C.</author><author>Bradford, D.</author></authors></contributors><auth-address>Division of Pulmonary and Critical Care Medicine, Department of Medicine, Medical University of South Carolina, 96 Jonathan Lucas St, PO Box 250630, Charleston, SC, USA. silvestri@musc.edu</auth-address><titles><title>Attitudes towards screening for lung cancer among smokers and their non-smoking counterparts</title><secondary-title>Thorax</secondary-title><alt-title>Thorax</alt-title></titles><periodical><full-title>Thorax</full-title><abbr-1>Thorax</abbr-1></periodical><alt-periodical><full-title>Thorax</full-title><abbr-1>Thorax</abbr-1></alt-periodical><pages>126-30</pages><volume>62</volume><number>2</number><keywords><keyword>Adult</keyword><keyword>Aged</keyword><keyword>*Attitude to Health</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/*diagnosis/prevention &amp; control/psychology</keyword><keyword>Male</keyword><keyword>Mass Screening/*psychology</keyword><keyword>Middle Aged</keyword><keyword>Smoking/*psychology</keyword></keywords><dates><year>2007</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0040-6376 (Print)&#xD;0040-6376 (Linking)</isbn><accession-num>17101739</accession-num><urls><related-urls><url> reported that smokers perceived fewer benefits and were less likely to consider lung cancer screening compared to non-smokers ADDIN EN.CITE <EndNote><Cite><Author>Silvestri</Author><Year>2007</Year><RecNum>93</RecNum><DisplayText>(92)</DisplayText><record><rec-number>93</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">93</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Silvestri, G. A.</author><author>Nietert, P. J.</author><author>Zoller, J.</author><author>Carter, C.</author><author>Bradford, D.</author></authors></contributors><auth-address>Division of Pulmonary and Critical Care Medicine, Department of Medicine, Medical University of South Carolina, 96 Jonathan Lucas St, PO Box 250630, Charleston, SC, USA. silvestri@musc.edu</auth-address><titles><title>Attitudes towards screening for lung cancer among smokers and their non-smoking counterparts</title><secondary-title>Thorax</secondary-title><alt-title>Thorax</alt-title></titles><periodical><full-title>Thorax</full-title><abbr-1>Thorax</abbr-1></periodical><alt-periodical><full-title>Thorax</full-title><abbr-1>Thorax</abbr-1></alt-periodical><pages>126-30</pages><volume>62</volume><number>2</number><keywords><keyword>Adult</keyword><keyword>Aged</keyword><keyword>*Attitude to Health</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/*diagnosis/prevention &amp; control/psychology</keyword><keyword>Male</keyword><keyword>Mass Screening/*psychology</keyword><keyword>Middle Aged</keyword><keyword>Smoking/*psychology</keyword></keywords><dates><year>2007</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0040-6376 (Print)&#xD;0040-6376 (Linking)</isbn><accession-num>17101739</accession-num><urls><related-urls><url>;. Non-participants in the Dutch-Belgian Randomised Controlled Lung Cancer Screening Trial (NELSON) were more likely to be female and former smokers, and to have a lower perceived risk of lung cancer and less positive beliefs about the benefits of lung screening compared to participants. ADDIN EN.CITE <EndNote><Cite><Author>van den Bergh</Author><Year>2009</Year><RecNum>94</RecNum><DisplayText>(93)</DisplayText><record><rec-number>94</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">94</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>van den Bergh, K. A.</author><author>Essink-Bot, M. L.</author><author>van Klaveren, R. J.</author><author>de Koning, H. J.</author></authors></contributors><auth-address>Dept of Public Health, Erasmus MC, University Medical Centre Rotterdam, The Netherlands. k.vandenbergh@erasmusmc.nl</auth-address><titles><title>Informed participation in a randomised controlled trial of computed tomography screening for lung cancer</title><secondary-title>Eur Respir J</secondary-title><alt-title>The European respiratory journal</alt-title></titles><periodical><full-title>Eur Respir J</full-title><abbr-1>The European respiratory journal</abbr-1></periodical><alt-periodical><full-title>Eur Respir J</full-title><abbr-1>The European respiratory journal</abbr-1></alt-periodical><pages>711-20</pages><volume>34</volume><number>3</number><keywords><keyword>Aged</keyword><keyword>Female</keyword><keyword>*Health Knowledge, Attitudes, Practice</keyword><keyword>Humans</keyword><keyword>Informed Consent</keyword><keyword>Logistic Models</keyword><keyword>Lung Neoplasms/*radiography</keyword><keyword>Male</keyword><keyword>*Mass Screening</keyword><keyword>Middle Aged</keyword><keyword>*Patient Participation</keyword><keyword>Questionnaires</keyword><keyword>*Randomized Controlled Trials as Topic</keyword><keyword>*Tomography, X-Ray Computed</keyword></keywords><dates><year>2009</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>1399-3003 (Electronic)&#xD;0903-1936 (Linking)</isbn><accession-num>19282345</accession-num><urls><related-urls><url> In the first 88,897 individuals approached to take part in UKLS, participation was more likely in ex-smokers and those in higher socioeconomic groups. ADDIN EN.CITE <EndNote><Cite><Author>McRonald</Author><Year>2014</Year><RecNum>95</RecNum><DisplayText>(94)</DisplayText><record><rec-number>95</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">95</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>McRonald, F. E.</author><author>Yadegarfar, G.</author><author>Baldwin, D. R.</author><author>Devaraj, A.</author><author>Brain, K. E.</author><author>Eisen, T.</author><author>Holemans, J. A.</author><author>Ledson, M.</author><author>Screaton, N. J.</author><author>Rintoul, R. C.</author><author>Hands, C. J.</author><author>Lifford, K.</author><author>Whynes, D. K.</author><author>Kerr, K. M.</author><author>Page, R.</author><author>Parmar, M.</author><author>Wald, N.</author><author>Weller, D.</author><author>Williamson, P. R.</author><author>Myles, J.</author><author>Hansell, D. M.</author><author>Duffy, S. W.</author><author>Field, J. K.</author></authors></contributors><auth-address>1Molecular and Clinical Cancer Medicine, University of Liverpool.</auth-address><titles><title>The UK Lung Screen (UKLS): demographic profile of first 88,897 approaches provides recommendations for population screening</title><secondary-title>Cancer Prev Res (Phila)</secondary-title><alt-title>Cancer prevention research</alt-title></titles><periodical><full-title>Cancer Prev Res (Phila)</full-title><abbr-1>Cancer prevention research</abbr-1></periodical><alt-periodical><full-title>Cancer Prev Res (Phila)</full-title><abbr-1>Cancer prevention research</abbr-1></alt-periodical><dates><year>2014</year><pub-dates><date>Jan 17</date></pub-dates></dates><isbn>1940-6215 (Electronic)&#xD;1940-6215 (Linking)</isbn><accession-num>24441672</accession-num><urls><related-urls><url> Other barriers may include fears about lung screening tests and radiation exposure, and fatalistic beliefs about lung cancer.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Kb25uYWxhZ2FkZGE8L0F1dGhvcj48WWVhcj4yMDEyPC9Z

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ADDIN EN.CITE.DATA 88 and the Danish Lung Cancer Screening Trial (DLCST). ADDIN EN.CITE <EndNote><Cite><Author>Aggestrup</Author><Year>2012</Year><RecNum>92</RecNum><DisplayText>(85)</DisplayText><record><rec-number>92</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">92</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Aggestrup, L. M.</author><author>Hestbech, M. S.</author><author>Siersma, V.</author><author>Pedersen, J. H.</author><author>Brodersen, J.</author></authors></contributors><auth-address>The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.</auth-address><titles><title>Psychosocial consequences of allocation to lung cancer screening: a randomised controlled trial</title><secondary-title>BMJ Open</secondary-title><alt-title>BMJ open</alt-title></titles><periodical><full-title>BMJ Open</full-title><abbr-1>BMJ open</abbr-1></periodical><alt-periodical><full-title>BMJ Open</full-title><abbr-1>BMJ open</abbr-1></alt-periodical><pages>e000663</pages><volume>2</volume><number>2</number><dates><year>2012</year></dates><isbn>2044-6055 (Electronic)</isbn><accession-num>22382119</accession-num><urls><related-urls><url> In NELSON, the intervention and control groups were equivalent in a range of psychosocial outcomes, both general and cancer-specific.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gZGVuIEJlcmdoPC9BdXRob3I+PFllYXI+MjAxMTwv

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ADDIN EN.CITE.DATA 88 Similarly, the DLCST found no differential effects of trial allocation on psychosocial outcomes at one year follow-up. ADDIN EN.CITE <EndNote><Cite><Author>Aggestrup</Author><Year>2012</Year><RecNum>92</RecNum><DisplayText>(85)</DisplayText><record><rec-number>92</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">92</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Aggestrup, L. M.</author><author>Hestbech, M. S.</author><author>Siersma, V.</author><author>Pedersen, J. H.</author><author>Brodersen, J.</author></authors></contributors><auth-address>The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.</auth-address><titles><title>Psychosocial consequences of allocation to lung cancer screening: a randomised controlled trial</title><secondary-title>BMJ Open</secondary-title><alt-title>BMJ open</alt-title></titles><periodical><full-title>BMJ Open</full-title><abbr-1>BMJ open</abbr-1></periodical><alt-periodical><full-title>BMJ Open</full-title><abbr-1>BMJ open</abbr-1></alt-periodical><pages>e000663</pages><volume>2</volume><number>2</number><dates><year>2012</year></dates><isbn>2044-6055 (Electronic)</isbn><accession-num>22382119</accession-num><urls><related-urls><url> Although those in the control group reported slightly worse psychosocial outcomes on some measures at follow-up, the difference between groups was not significant when examining the mean increase in scores on each scale. The National Cancer Institute’s Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCT)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UYXlsb3I8L0F1dGhvcj48WWVhcj4yMDA0PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 87 reported poorer health-related quality of life in the intervention groups on one measure (mental component of SD-12), but no difference on any other psychosocial measure.6.1.4Psychosocial impact of lung screening resultsRecent trials which have examined the psychosocial effects of lung screening results indicate that the negative impact of receiving an unfavourable result, especially in relation to cancer-specific distress, tends to diminish over time. The NELSON trial reported poorer quality of life, increased general anxiety and clinically elevated levels of cancer-specific distress at two months follow-up in high-risk individuals receiving an indeterminate CT scan result compared to those receiving a negative (normal) result. However, these effects did not persist at six months follow-up, i.e. after the second screening round.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gZGVuIEJlcmdoPC9BdXRob3I+PFllYXI+MjAxMTwv

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ADDIN EN.CITE.DATA 88, 89 A similar pattern was observed in the Pittsburgh Lung Screening Study (PLuSS)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CeXJuZTwvQXV0aG9yPjxZZWFyPjIwMDg8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 86 with a temporary increase in stated anxiety in those with an indeterminate screening result, although cancer-related fear did not return to baseline levels at one year follow-up. Participants in the PLCO Cancer Screening Trial, who received at least one abnormal result, across any of the cancers being screened, reported higher cancer-specific distress in the short-term compared to those who had received all normal screening results.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UYXlsb3I8L0F1dGhvcj48WWVhcj4yMDA0PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 87 No significant differences were found on the mental or physical components of the SF-12, nor were there differences on any of the measures at nine months follow-up, suggesting that negative psychosocial consequences of lung screening results are not long lasting. 6.2AimTo assess the short-term psychosocial consequences of lung cancer screening. 6.3ObjectivesSpecific objectives were to examine:Factors associated with trial non-uptake in high risk individualsThe short-term and long-term impact of trial allocation on lung cancer-specific distress (primary outcome), anxiety, depression and decision satisfaction (secondary outcomes)The impact of baseline CT scan result on lung cancer-specific distress, anxiety, depression and decision satisfactionThe long-term impact of screening outcome on lung cancer-specific distress, anxiety, depression and decision satisfactionThe short-term and long-term impact of trial allocation on the primary outcome adjusting for effect modifiers.6.4HypothesesIntervention arm participants will report higher short-term cancer distress compared to those in the control arm. In particular, those intervention arm participants who receive a positive result of their baseline CT scan will report higher short-term cancer distress than those with negative (normal) results.There will be no difference in long-term cancer distress between trial arms or between screening outcome groups.6.5Methods6.5.1Participants A random sample of 247,354 individuals aged between 50 and 75 years of age from six primary care trusts (PCTs) (three from the Liverpool area and three from the Cambridge area) were initially invited to take part in the UKLS trial by completing a risk assessment questionnaire based on the Liverpool Lung Project Risk Prediction Model. Individuals who were identified as being at high risk of lung cancer were then sent further information about the trial and invited to participate. Following completion of a further screening questionnaire to identify trial eligibility, people meeting the criteria were invited to attend the recruitment centre. High risk individuals who attended the recruitment centre, were confirmed as eligible, and consented to take part in the trial were included in the psychosocial outcomes component of the study. Individuals who actively declined to take part by completing an optional non-participation questionnaire are referred to as negative responders.6.5.2ProcedureAt the UKLS trial recruitment centre, participants completed a baseline touch screen questionnaire which included a number of psychosocial measures (referred to as T0). Participants were sent a follow-up psychosocial questionnaire (T1) approximately two weeks after receiving either 1) the letter detailing that they had been assigned to the control arm of the trial (control group) or 2) the baseline CT scan result letter (intervention group). A freepost envelope was included with the questionnaire pack for participants to return their completed questionnaires to the data management company RADAR. Between September 2012 and January 2013, a subset of just over 600 participants who did not return their T1 questionnaire received a reminder. A further follow-up questionnaire (T2) and freepost envelope were sent in January 2014 (10 to 27 months after attending the recruitment centre) to all participants who were still alive and not “off study”.6.5.3 Measures6.5.3.1 Lung cancer-specific distressThe revised 6-item Cancer Worry ScalePEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MZXJtYW48L0F1dGhvcj48WWVhcj4xOTkzPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 58, 59 was adapted to measure lung cancer-specific distress. The scale includes six items each measured on a 4-point likert scale (score range 6 to 24). Participants were asked to complete the scale thinking about how they felt in the past week. The scale was found to have good internal consistency (T0 α=.81, T1 α=.85, T2 α=.85). A score above 12.5 corresponds to a clinically significant threshold score on the General Health Questionnaire-28. ADDIN EN.CITE <EndNote><Cite><Author>Brain</Author><Year>2011</Year><RecNum>98</RecNum><DisplayText>(97)</DisplayText><record><rec-number>98</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">98</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Brain, K.</author><author>Parsons, E.</author><author>Bennett, P.</author><author>Cannings-John, R.</author><author>Hood, K.</author></authors></contributors><auth-address>Institute of Medical Genetics, Clinical Epidemiology Interdisciplinary Research Group, Cardiff University School of Medicine, Cardiff, UK. brainke@cardiff.ac.uk</auth-address><titles><title>The evolution of worry after breast cancer risk assessment: 6-year follow-up of the TRACE study cohort</title><secondary-title>Psychooncology</secondary-title><alt-title>Psycho-oncology</alt-title></titles><periodical><full-title>Psychooncology</full-title><abbr-1>Psycho-oncology</abbr-1></periodical><alt-periodical><full-title>Psychooncology</full-title><abbr-1>Psycho-oncology</abbr-1></alt-periodical><pages>984-91</pages><volume>20</volume><number>9</number><keywords><keyword>Adult</keyword><keyword>*Anxiety</keyword><keyword>Breast Neoplasms/genetics/*psychology</keyword><keyword>Female</keyword><keyword>Follow-Up Studies</keyword><keyword>Genetic Predisposition to Disease/*psychology</keyword><keyword>*Health Behavior</keyword><keyword>Humans</keyword><keyword>Mammography/*psychology</keyword><keyword>Middle Aged</keyword><keyword>Questionnaires</keyword><keyword>Risk Assessment</keyword><keyword>Risk Factors</keyword></keywords><dates><year>2011</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>1099-1611 (Electronic)&#xD;1057-9249 (Linking)</isbn><accession-num>20677331</accession-num><urls><related-urls><url> One question from the Cancer Worry Scale was included in the non-participation questionnaire (“How concerned are you, if at all, about the possibility of getting lung cancer some day?”). For comparisons between trial participants and negative responders who completed the non-participation questionnaire, responses to this question were re-coded into three categories of affective risk perception: none (“not at all” concerned), lower (“somewhat” concerned) and higher (“moderately” or “very” concerned).6.5.3.2General anxiety and depression The Hospital Anxiety and Depression Scale (HADS) ADDIN EN.CITE <EndNote><Cite><Author>Zigmond</Author><Year>1983</Year><RecNum>57</RecNum><DisplayText>(57)</DisplayText><record><rec-number>57</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">57</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zigmond, A. S.</author><author>Snaith, R. P.</author></authors></contributors><titles><title>The hospital anxiety and depression scale</title><secondary-title>Acta Psychiatr Scand</secondary-title><alt-title>Acta psychiatrica Scandinavica</alt-title></titles><periodical><full-title>Acta Psychiatr Scand</full-title><abbr-1>Acta psychiatrica Scandinavica</abbr-1></periodical><alt-periodical><full-title>Acta Psychiatr Scand</full-title><abbr-1>Acta psychiatrica Scandinavica</abbr-1></alt-periodical><pages>361-70</pages><volume>67</volume><number>6</number><edition>1983/06/01</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Anxiety Disorders/*diagnosis</keyword><keyword>Depressive Disorder/*diagnosis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Interview, Psychological</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Outpatient Clinics, Hospital</keyword><keyword>Outpatients/*psychology</keyword><keyword>Patients/*psychology</keyword><keyword>*Psychiatric Status Rating Scales</keyword><keyword>Psychometrics</keyword></keywords><dates><year>1983</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>0001-690X (Print)&#xD;0001-690X (Linking)</isbn><accession-num>6880820</accession-num><urls><related-urls><url> was used to measure general levels of anxiety and depression. Anxiety and depression sub-scales each include seven items on a 0 to 3 scale, anchored to how participants felt in the last week (score range 0 to 21). Good internal consistency was found both anxiety (T0 α=.81, T1 α=.87, T2 α=.87) and depression (T0 α=.73, T1 α=.84, T2 α=.84). Scores of 0-7 were classified as ‘normal’, 8-10 as ‘mild’ anxiety or depression, and 11-14 and 15-21 as ‘moderate’ and ‘severe’ respectively. ADDIN EN.CITE <EndNote><Cite><Author>Zigmond</Author><Year>1983</Year><RecNum>57</RecNum><DisplayText>(57)</DisplayText><record><rec-number>57</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">57</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zigmond, A. S.</author><author>Snaith, R. P.</author></authors></contributors><titles><title>The hospital anxiety and depression scale</title><secondary-title>Acta Psychiatr Scand</secondary-title><alt-title>Acta psychiatrica Scandinavica</alt-title></titles><periodical><full-title>Acta Psychiatr Scand</full-title><abbr-1>Acta psychiatrica Scandinavica</abbr-1></periodical><alt-periodical><full-title>Acta Psychiatr Scand</full-title><abbr-1>Acta psychiatrica Scandinavica</abbr-1></alt-periodical><pages>361-70</pages><volume>67</volume><number>6</number><edition>1983/06/01</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Anxiety Disorders/*diagnosis</keyword><keyword>Depressive Disorder/*diagnosis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Interview, Psychological</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Outpatient Clinics, Hospital</keyword><keyword>Outpatients/*psychology</keyword><keyword>Patients/*psychology</keyword><keyword>*Psychiatric Status Rating Scales</keyword><keyword>Psychometrics</keyword></keywords><dates><year>1983</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>0001-690X (Print)&#xD;0001-690X (Linking)</isbn><accession-num>6880820</accession-num><urls><related-urls><url> 6.5.3.3Decision satisfactionSatisfaction with the decision to participate in the trial was measured using the Satisfaction with Decision ScalePEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Ib2xtZXMtUm92bmVyPC9BdXRob3I+PFllYXI+MTk5Njwv

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ADDIN EN.CITE.DATA 98 6-item scale with five response categories ranging from ‘strongly disagree’ to ‘strongly agree’. Scale scores were summed and then averaged so that possible scores ranged from 1 to 5 (T0 α=.94, T1 α=.95, T2 α=.96). Due to a bi-modal distribution in the current study, a binary variable was created to reflect lower decision satisfaction (score <5, “not very satisfied”) and higher decision satisfaction (score=5, “very satisfied”). 6.5.3.4Demographic VariablesEducational attainment, marital status and ethnic group were included in the T0 questionnaire. Age (around the time of risk calculation) and gender were provided by the PCTs (via the data management company). For analyses examining barriers to trial uptake, age was categorised into three groups: younger age (≤65 years), recently retired (66-70 years) and older age (≥71 years). IMD scores and ranks were established by the data management company using participants’ postcodes. IMD ranks were analysed using standard quintiles based on England-wide population data; quintile 1: 1-6496, quintile 2: 6497-12993, quintile 3: 12994-19489 quintile 4: 19490-25986 and quintile 5: 25987-32482. Quintile 1 reflects those most deprived and quintile 5 those least deprived.6.5.3.5Smoking statusSmoking status was collected in the UKLS questionnaire 1 (UKLSQ1). Participants were identified as current smokers, ex-smokers or never smokers. Very few high-risk participants had never smoked, hence this category was excluded during analyses.6.5.3.6 Experience of lung cancerParticipants were asked in the T0 questionnaire whether they had a friend or close family member who had been diagnosed with lung cancer. Five responses were available and were categorised into two groups “yes” (included responses “yes, self”, “yes, someone close”, “yes self and someone close”, “yes, prefer not to say who”) and “no” (response “no”).6.5.3.7CT scan resultParticipants in the intervention group were categorised according to their CT scan result both at baseline and longer-term follow up (result of their most recent scan):Negative: those who received a negative (normal) result.Negative with incidental finding: those who received a significant incidental finding but no lung abnormality.Positive for repeat scan: those who were recommended to attend for a repeat scan in 3 or 12 months.Positive for MDT referral: those referred to the multidisciplinary team (MDT) with a major lung abnormality.6.5.3.8 Long-term screening outcome Screening outcome has been defined based on participants’ screening history and the outcome of their final CT scan (where relevant) at the time of T2 questionnaire completion:True negative: those who received a negative (normal) result and no lung cancer diagnosed.False positive: those who were recommended to attend for a repeat scan in 3 or 12 months, but then had a final negative (normal) result and no lung cancer diagnosed.True positive: those who were recommended to attend for a repeat scan in 3 or 12 months or referred to the MDT with a major lung abnormality, and lung cancer was diagnosed.Incidental finding: those who received a significant incidental finding (either at baseline scan or repeat scan) but no lung cancer diagnosed.Participants who had a MDT referral but no lung cancer diagnosed were excluded from long-term outcome analyses, as were those still awaiting their final scan or result at the time of completing T2. Those whose results were not classified were also excluded as they did not follow the standard care pathway and / or their psychosocial situation did not reflect their categorisation in the main trial, for example mesothelioma patients were classified as Category 1 (end of screening).6.5.4Statistical analysesAnalyses were conducted using SPSS v.20. A mean replacement strategy was used where participants were missing data on the psychosocial variables. Scale scores were assessed for normality and log transformed in cases of non-normal distribution (cancer distress, anxiety and depression). Statistical associations between individual characteristics and trial participation were examined using univariable and multivariable regression modelling. Attrition bias was assessed using chi-squared and independent t-tests.6.5.4.1 Primary analyses At each follow-up endpoint, psychosocial outcomes of trial allocation and screening results were assessed using ANCOVAs (adjusted for baseline psychosocial score) and chi-squared tests as appropriate. The assumptions of ANCOVA, including homogeneity of regression slopes and linearity, were tested. Regression slopes for lung cancer distress scores were heterogeneous at short-term follow-up, therefore subgroup analyses were performed separately for high and low cancer distress thresholds (above or below 12.5). Sensitivity analyses were performed to assess any confounding effects of data timing issues. At short-term assessment, participants were identified as protocol deviations and excluded from analyses if they completed their T1 questionnaire less than 10 days after their baseline scan result letter or control arm allocation letter was sent. At long-term assessment, participants who received a final scan or scan result within the two weeks prior to or succeeding T2 completion were identified as protocol deviations and were excluded from the data set. To account for multiple testing, a conservative p value of 0.01 was used to indicate statistical significance. Effect size was calculated using the 2 statistic (where a value of .01 represents a weak effect, .06 represents a moderate effect, and .14 represents a strong effect). Following significant ANCOVA results, post hoc pairwise comparisons (using the Sidak correction and p value of 0.05 to indicate statistical significance) were carried out to identify group differences. 6.5.4.2 Secondary analysesLinear mixed effects modelling was performed to account for both short-term and long-term cancer distress. The model evaluated the impact of trial allocation after adjustment for baseline cancer distress, with a random subject intercept and slope separately for each outcome. Sensitivity analyses were conducted (separately for participants with low or high baseline cancer distress scores), each with different cancer distress effect modifiers included:Univariable analysis evaluating the impact of trial allocation adjusting for baseline cancer distress only and each additional effect modifier separately. Preconceived scientifically justified multivariable analysis evaluating the impact of trial allocation after adjustment for baseline cancer distress and other scientifically important effect modifiers: age group, smoking, deprivation quintile, and time since attended recruitment centre. Preconceived scientifically justified multivariable analysis evaluating the impact of trial allocation after adjustment for baseline cancer distress and all other scientifically and statistically important effect modifiers. This involved a forward stepped model fitting process, assuming analysis (2) as the base model using the maximum likelihood approach for handling degrees of freedom and nested models. Effect modifiers were included if a likelihood ratio was significant at p≤0.01 and were sequentially introduced in order of statistical importance. Covariates considered for inclusion as effect modifiers were trial site, gender, ethnicity, marital group, and experience of lung cancer. All trial group-by-main effect interactions were then assessed for statistical importance. After the optimal and parsimonious statistical model was found, parameter estimates, standard errors and p values were estimated using the residual maximum likelihood approach to handling degrees of freedom.For all the above, effect modifiers are summarised, interpreted and compared between nested analyses. 6.6Results6.6.1Trial participationA flow diagram of participation in the trial is shown in Figure 15. In total, 4,061 individuals (5.3% of 75,958 positive responders; 46.5% of all high risk positive responders) attended the recruitment clinic and were consented into the trial. Of these, 4,039 trial participants completed baseline T0 psychosocial questionnaires: 4,037 were randomised (n=2,018 intervention, n=2,019 control) and 3,232 of the randomised participants completed T1 psychosocial questionnaires (n=1,653/84% intervention, n=1,579/78% control). At T2, 2,855 participants completed psychosocial questionnaires (n=1,553/82% intervention, n=1,302/65% control).8453896605270-2432057781940T0Q = psycho-social baseline touch screen questionnaire (collected at recruitment centre)T1Q = psycho-social short-term follow-up questionnaire (sent 2 weeks after result or control arm letter)T2Q = psycho-social longer-term follow-up questionnaire (sent January 2014)00T0Q = psycho-social baseline touch screen questionnaire (collected at recruitment centre)T1Q = psycho-social short-term follow-up questionnaire (sent 2 weeks after result or control arm letter)T2Q = psycho-social longer-term follow-up questionnaire (sent January 2014)39655756900235T2Q completed 1302 (/1993=65.3%)T2Q not completed 691Protocol deviation 34 (no T0 data 8, T2Q not sent 26, protocol deviation at T2 0)00T2Q completed 1302 (/1993=65.3%)T2Q not completed 691Protocol deviation 34 (no T0 data 8, T2Q not sent 26, protocol deviation at T2 0)-3295656857380T2Q completed 1553 (/1888=82.3%)T2Q not completed 335Protocol deviation 140 (no T0 data 10 T2Q not sent 57, protocol deviation at T2 73)00T2Q completed 1553 (/1888=82.3%)T2Q not completed 335Protocol deviation 140 (no T0 data 10 T2Q not sent 57, protocol deviation at T2 73)3856252664530530515445475768T1Q completed 1579 (/2016=78.3%)T1Q not completed 437Protocol deviation 11 (no T0 data 8, T1Q not sent 2, protocol deviation at T1 1)00T1Q completed 1579 (/2016=78.3%)T1Q not completed 437Protocol deviation 11 (no T0 data 8, T1Q not sent 2, protocol deviation at T1 1)-3296095497033T1Q completed 1653 (/1965=84.1%)T1Q not completed 312Protocol deviation 63 (no T0 data 10, T1 not sent 34, protocol deviation at T1 19)00T1Q completed 1653 (/1965=84.1%)T1Q not completed 312Protocol deviation 63 (no T0 data 10, T1 not sent 34, protocol deviation at T1 19)00Information packs sent 247,354No response 148,608 (60.1%)Positive response 75,958 (30.7%) Negative response 22,788 (9.2%)High risk 8729 (11.5%)Positive response 5,967 (68.4%)No response 2,008 (19.4%)Negative response 754 (12.2%) 2nd Information packs sent to high risk positive responders 00Information packs sent 247,354No response 148,608 (60.1%)Positive response 75,958 (30.7%) Negative response 22,788 (9.2%)High risk 8729 (11.5%)Positive response 5,967 (68.4%)No response 2,008 (19.4%)Negative response 754 (12.2%) 2nd Information packs sent to high risk positive responders 754912526311637958235243372372935542423917549124242391002009081378519119989213667863755650307784531232104520244Control group 2027T0Q completed 2019 (/2027=99.6%)(no T0 data 8)00Control group 2027T0Q completed 2019 (/2027=99.6%)(no T0 data 8)-1911354519295CT-Scan group 2028T0Q completed 2018 (/2028=99.5%)(no T0 data 10)146700CT-Scan group 2028T0Q completed 2018 (/2028=99.5%)(no T0 data 10)1467right34438446 not randomised (4 ineligible, 2 no randomisation partner)006 not randomised (4 ineligible, 2 no randomisation partner)1425043958293Randomisation 4,05500Randomisation 4,0551320803360717Attended clinic and consented to UKLS 4,06100Attended clinic and consented to UKLS 4,061Figure 15: Trial participation flow diagram6.6.1.1 Barriers to Trial UptakeOf 8,729 high risk individuals, the current analysis compared 4,061 individuals who attended recruitment clinic and consented to participate with 2,762 individuals who declined participation (i.e. 754 negative responders, 2,008 non-responders). Of the negative responders, five were subsequently excluded due to reported gender discrepancies and one was reported as deceased.Age, gender, smoking and socioeconomic group were statistically significantly associated with trial uptake (see Table 14). Women were less likely to take part compared with men (OR=0.64; p<.001), and current smokers were less likely to take part than ex-smokers (OR=0.70, p<.001). Older individuals were less likely to participate in the trial compared with younger individuals aged ≤65 years (OR=0.73, p<.001) and those recently retired (OR=0.76, p<.001), but the difference in uptake between younger individuals and those recently retired was not statistically significant. Individuals in the highest socioeconomic group (quintile 5) were most likely to participate. Individuals in the lowest quintile were almost twice as likely to decline trial participation compared to those in the highest quintile (OR=0.56, p<.001).Individuals with a higher affective risk perception were less likely to take part in the trial compared with individuals reporting no or lower levels of affective risk perception (OR=0.52, p<.001 and OR=0.64, p<.001 respectively after adjusting for age, gender, smoking and socioeconomic group). There was no statistically significant difference between none and lower levels of affective risk perception (see Table 15). There were no significant interactions between the included main effects (e.g. the interaction between gender and affective risk was not statistically important, p=0.81).Table 14: Univariable and multivariable analyses of factors influencing trial uptake in high risk individualsNon-participants (n=2756)?n (%)Trial participants (n=4061)?n (%)Univariable OR (95% CI)p valueMultivariable OR (95% CI) ?p valueGenderMale1770 (64)3041 (75)1.00 (REF)Female986 (36)1020 (25)0.60 (0.54-0.67)<0.001***0.64 (0.58-0.71)<0.001***Age rangeYounger age (≤65 years)838 (30)1249 (31)1.00 (REF)Recently retired (66-70 years)1087 (39)1742 (43)1.08 (0.96-1.21)0.221.05 (0.93-1.18)0.47Older population (≥71 years)831 (30)1070 (26)0.86 (0.76-0.98)0.020.73 (0.64-0.80)<0.001***Older compared to recently retired0.80 (0.71-0.90)<0.001***0.70 (0.62-0.79)<0.001***Smoking Current smoker1334 (48)1568 (39)0.67 (0.61-0.74)<0.001***0.70 (0.63-0.78)<0.001***Ex-smoker1418 (51)2591 (61)1.00 (REF)Never smoked?4 (<1)2 (<1)(-)Socioeconomic groupQuintile 1 (most deprived)924 (34)1090 (27)0.52 (0.45-0.60)<0.001***0.56 (0.49-0.65)<0.001***Quintile 2 448 (16)487 (12)0.48 (0.40-0.57)<0.001***0.49 (0.42-0.59)<0.001***Quintile 3483 (18)723 (18)0.66 (0.56-0.77)<0.001***0.68 (0.58-0.80)<0.001***Quintile 4 447 (16)732 (18)0.72 (0.61-0.85)<0.001***0.73 (0.62-0.86)<0.001***Quintile 5 (least deprived)453 (16)1029 (25)1.00 (REF)?Ns vary within each cell due to missing data. Percentages are calculated based on available data.?Adjusted for all other variables in the model.?Smoking data <2% never smoked therefore excluded from analysis.***p<0.001Table 15: Univariable and multivariable analyses of level of affective risk perception influencing trial uptake in high risk individualsNegative responders (n=748)n (%)Trial participants (n=4061)n (%)Univariable OR (95% CI)p valueMultivariable OR (95% CI) ?p valueAffective risk perceptionNone129 (19)1054 (26)1.00 (REF)Lower213 (32)1493 (37)0.86 (0.69-1.09)0.220.82 (0.65-1.04)0.09Higher329 (49)1478 (37)0.55 (0.45-0.69)<0.001***0.52 (0.42-0.65)<0.001***Higher compared with lower0.64 (0.53-0.77)<0.001***0.64 (0.53-0.77)<0.001***?Adjusted for age, gender, smoking and socioeconomic group***p<0.0016.6.2 Attrition bias6.6.2.1 Baseline touch-screen questionnaire completionDescriptive statistics for those who did (n=4,039) and did not (n=22) complete T0 questionnaires at the recruitment centre are shown in Table 33 (see Appendix 7). Statistical comparisons were not conducted due to the small number of non-completers.6.6.2.2 Baseline CT scan attendanceOf 2,018 participants in the intervention arm, 31 did not attend their baseline CT scan. Descriptive statistics for scan attendees and non-attendees are provided in Table 34 (see Appendix 7). A trend towards lower decision satisfaction was observed in non-attendees, but this should be interpreted with caution due to the small sample size, which precluded formal statistical testing.6.6.2.3 Follow-up questionnaire completion Statistical comparisons between those who did and did not complete short-term and long-term follow-up psychosocial questionnaires, respectively, are displayed in Tables 16 and 17. At both time points, questionnaire completion was associated with older age (p≤.01), being male (p≤.01), being married or cohabiting (p<.001), higher educational attainment (p<.001), being an ex-smoker (compared to being a current smoker, p<.001) and taking part in the Papworth site (p≤.001). Note that a large amount of data on educational level were missing or non-informative, therefore the results for this variable should be interpreted with caution. Questionnaire completion was also associated with deprivation (p<.001), with a greater proportion of completers in the highest IMD quintile at T1 and T2 and a greater proportion of non-completers in the lowest quintile at T2. Differences between completers and non-completers in ethnicity were not statistically significant at either follow-up.A greater proportion of non-completers were from the control arm at each time point (both at p<.01). While this suggests differential drop-out, it should be noted that protocol exclusions were more frequent in the screening arm. Those who completed follow-up questionnaires reported significantly lower baseline scores on all psychosocial measures compared to non-completers (all at p<.001). However, the absolute differences were very small and scores were in the low range for both groups. Lung cancer experience was significantly associated with questionnaire completion, with a great proportion of non-completers having had experience of lung cancer (p≤.01). Differences between completers and non-completers in decision satisfaction were not statistically significant at either follow-up. Table 16: Comparison of T1 completers and non-completers T1 completers (n=3232) ?n (%) or mean (SD)T1 non-completers (n=749) ?n (%) or mean (SD)Test statistic (p-value)Trial allocationIntervention 1653 (51)312 (42)χ2(1) = 21.9 (<.001)Control 1579 (49) 437 (58)SiteLiverpool1585 (49)420 (56)χ2(1) = 12.0 (.001)Papworth1647 (51)329 (44)Age67.73 (3.98)67.28 (4.46)t(1041.7) = -2.5 (.01)GenderMale 2446 (76) 533 (71)χ2(1) = 6.6 (.01)Female786 (24) 216 (29)Education^ Up to GCSE/O level or equivalent 1007 (43)334 (57)χ2(1) = 35.4 (<.001)Beyond GCSE/O level or equivalent 1310 (57) 250 (43)Ethnicity White 3190 (99)739 (99)χ2(1) = 1.9 (.17)Non-white26 (1) 10 (1)Marital status Married /cohabiting 2410 (75)508 (68)χ2(1) = 14.6 (<.001)Not married/cohabiting* 814 (25)240 (32)IMDQuintile 1 843 (26)222 (30)χ2(4) = 23.6 (<.001)Quintile 2 367 (11)109 (15)Quintile 3 574 (18)135 (18)Quintile 4 578 (18) 141 (19)Quintile 5 870 (27) 142 (19)Smoking status (UKLSQ1)Current smoker 1194 (37)338 (45)χ2(1) = 17.4 (<.001)Ex-smoker 2037 (63)410 (55)Never smoker 1 (<1)**1 (<1)**n/aExperience of lung cancer (T0)No 1870 (58)396 (53)χ2(1) = 6.3 (.01)Yes 1355 (42)352 (47)Cancer distress (T0) +2.16 (0.28)8.65 2.21 (0.31)9.16 t(3968) = 4.9 (<.001)Anxiety (T0) +1.52 (0.71)3.59 1.65 (0.71)4.20 t(3973) = 4.3 (<.001)Depression (T0) +1.27 (0.67)2.56 1.37 (0.66)2.94t(3971) = 3.7 (<.001)Decision satisfaction (T0 ) Not very satisfied 1916 (59)428 (57)χ2(1) = 1.0 (.32)Very satisfied 1308 (41)317 (43)?Ns vary within each cell due to missing data. Percentages are calculated based on available data.+log transformed scores in normal text and original scale scores in italics (analyses performed using log transformed scores).*not married/cohabiting = single, widowed, divorced/separated. **data excluded from analysis due to small cell size.^ A substantial amount of data were missing or uninformative for the education variable. Table 17: Comparison of T2 completers and non-completers T2 completers (n=2855) ?n (%) or mean (SD)T2 non-completers (n=1026) ?n (%) or mean (SD)Test statistic(p-value)Trial allocationIntervention1553 (54)335 (33)χ2(1) = 142.9 (<.001)Control1302 (46)691 (67)SiteLiverpool1336 (47)622 (61)χ2(1) = 57.7 (<.001)Papworth1519 (53)404 (39)Age67.79 (3.98)67.19 (4.31)t(1691.2) = -4.1 (<.001)GenderMale 2166 (76)736 (72)χ2(1) = 6.8 (.009)Female689 (24)290 (28)Education^ Up to GCSE/O level or equivalent 940 (44)367 (53)χ2(1) = 15.8 (<.001)Beyond GCSE/O level or equivalent1196 (56)330 (47)Ethnicity White 2825 (99)1004 (99)χ2(1) = 4.4 (.04)Non-white21 (1)15 (1)Marital status Married /cohabiting 2174 (76)672 (66)χ2(1) = 44.1 (<.001)Not married/cohabiting* 675 (24)352 (34)IMDQuintile 1668 (23)373 (36)χ2(4) = 73.1 (<.001)Quintile 2338 (12)126 (12)Quintile 3523 (18)170 (17)Quintile 4 545 (19)157 (15)Quintile 5781 (27)200 (19)Smoking status (UKLSQ1) Current smoker1009 (35)490 (48)χ2(1) = 50.0 (<.001)Ex-smoker1845 (65)536 (52)Never smoker1 (<1)**0 (0)**n/aExperience of lung cancer (T0)No1683 (59)522 (51)χ2(1) = 20.4 (<.001)Yes1166 (41)503 (49)Cancer distress (T0) +2.16 (0.28)8.642.20 (0.32)9.06t(1615.1) = 4.5 (<.001)Anxiety (T0) +1.51 (0.71)3.521.64 (0.71)4.17t(3873) = 5.2 (<.001)Depression (T0) +1.25 (0.66)2.511.38 (0.68)2.96t(3871) = 5.0 (<.001)Decision satisfaction (T0) Not very satisfied 1668 (59)612 (60)χ2(1) = 0.52 (.47)Very satisfied1179 (41)410 (40)?Ns vary within each cell due to missing data. Percentages are calculated based on available data.+log transformed scores in normal text and original scale scores in italics (analyses performed using log transformed scores).*not married/cohabiting = single, widowed, divorced/separated.**data excluded from analysis due to small cell size.^A substantial amount of data were missing or uninformative for the education variable.6.6.2.4 Baseline sample characteristicsComparisons were made between the intervention and control arm, including only those who completed T0 questionnaires and were therefore in the psychosocial sample. As indicated in Table 18, demographic characteristics and baseline psychosocial scores were similar for the trial arms due to the adequate randomisation process, hence test statistics were not warranted. Table 18: Baseline psychosocial sample characteristics by trial allocation Intervention(n = 2018) ?n (%) or mean (SD)Control (n = 2019) ?n (%) or mean (SD)SiteLiverpool1003 (50)1016 (50)Papworth1002 (50)1016 (50)Age67.72 (4.04)67.59 (4.13)GenderMale 1520 (75)1500 (74)Female498 (25)519 (26)Education^ Up to GCSE/O level or equivalent 678 (46)683 (46)Beyond GCSE/O level or equivalent788 (54)791 (54)Ethnicity White 1992 (99)1992 (99)Non-white18 (1)19 (1)Marital status Married /cohabiting 1483 (74)1471 (73)Not married/cohabiting* 528 (26)545 (27)IMDQuintile 1545 (27)533 (26)Quintile 2243 (12)242 (12)Quintile 3358 (18)361 (18)Quintile 4 353 (18)376 (19)Quintile 5519 (26)507 (25)Smoking status (UKLSQ1)Current smoker772 (38)787 (39)Ex-smoker1244 (62)1232 (61)Never smoker2 (<1)0 (0)Experience of lung cancer (T0)No1168 (58)1126 (56)Yes846 (42)889 (44)Cancer distress (T0) +2.17 (0.29)8.752.17 (0.29)8.74Anxiety (T0) +1.55 (0.71)3.721.54 (0.71)3.67Depression (T0) +1.30 (0.68)2.661.28 (0.67)2.61Decision satisfaction (T0) Not very satisfied 1228 (61)1158 (58)Very satisfied786 (39)853 (42)?Ns vary within each cell due to missing data. Percentages are calculated based on available data.+log transformed scores in normal text and original scale scores in italics.*not married/cohabiting = single, widowed, divorced/separated.^A substantial amount of data were missing or uninformative for the education variable.6.6.3 Short-term outcomes analysis The final sample for short-term psychosocial outcomes analysis (i.e. trial participants who had completed both T0 and T1 psychosocial questionnaires) consisted of 3232 individuals (see Figure 1). Mean age of participants was 67.7 years (see Table 16). The majority of participants were male (76%), were married or cohabiting (75%) and were of white ethnic origin (99%). A third (31%) of the sample were educated up to and including GCSE, O level or equivalent, and 40% were educated beyond (data on educational level were missing or non-informative for 28% of the sample). Most of the sample comprised of ex-smokers (63%). There was a spread of different levels of deprivation within the sample, with just over a quarter being in the highest quintile and just over a quarter being in the lowest quintile. Low mean levels of cancer distress, anxiety and depression were observed at baseline (see Tables 16 and 19). When categorised, 10% of the sample showed high levels of cancer distress at baseline, 8% showed moderate to severe levels of anxiety and 3% showed moderate to severe levels of depression. Just over 40% were very satisfied with their decision to take part in UKLS. Comparisons between trial allocation groups showed no significant differences in baseline characteristics (Table 19).Table 19: T1 sample baseline characteristics by trial allocation Intervention(n = 1653) ?n (%) or mean (SD)Control (n = 1579) ?n (%) or mean (SD)Test statistic (p-value)SiteLiverpool819 (50)766 (49)χ2(1) = 0.35 (.56)Papworth834 (51)813 (52)Age67.71 (3.94)67.75 (4.02)t(3230) = 0.30 (.77)GenderMale 1253 (76)1193 (76)χ2(1) = 0.03 (.87)Female400 (24)386 (24)Education^ Up to GCSE/O level or equivalent 516 (44)491 (43)χ2(1) = 0.40 (.53)Beyond GCSE/O level or equivalent654 (56)656 (57)Ethnicity White 1632 (99)1558 (99)χ2(1) = 0.01 (.91)Non-white13 (1)13 (1)Marital status Married /cohabiting 1239 (75)1171 (74)χ2(1) = 0.33 (.57)Not married/cohabiting* 409 (25)405 (26)IMDQuintile 1447 (27)396 (25)χ2(4) = 3.28 (.51)Quintile 2193 (12)174 (11)Quintile 3292 (18)282 (18)Quintile 4 280 (17)298 (19)Quintile 5441 (27)429 (27)Smoking status (UKLSQ1)Current smoker621 (38)573 (36)χ2(1) = 0.59 (.44)Ex-smoker1031 (62)1006 (64)Never smoker1 (<1)0 (0)n/aExperience of lung cancer (T0)No974 (59)896 (57)χ2(1) = 1.62 (.20)Yes675 (41)680 (43)Cancer distress (T0) + 2.16 (0.28)8.712.15 (0.28)8.60t(3223) = -1.26 (.21)Anxiety (T0) +1.53 (0.71)3.631.51 (0.71)3.55t(3225) = -0.74 (.46)Depression (T0) +1.28 (0.68)2.58 1.26 (0.67) 2.54t(3224) = -0.52 (.60)Decision satisfaction (T0) Not very satisfied 1000 (61)916 (58)χ2(1) = 1.82 (.18)Very satisfied651 (39)657 (42)?Ns vary within each cell due to missing data. Percentages are calculated based on available data.+log transformed scores in normal text and original scale scores in italics (analyses performed using log transformed scores).*not married/cohabiting = single, widowed, divorced/separated.^A substantial amount of data were missing or uninformative for the education variable.Hypothesis 1: Intervention arm participants will report higher short-term cancer distress compared to those in the control armAs shown in Table 20, different effects of trial allocation on T1 lung cancer distress were found for participants who scored above and below the distress threshold (12.5) at baseline. For those with low baseline distress, T1 distress scores were significantly higher in the intervention group, though not to clinical levels and with a very small effect size. For those with high baseline distress (326/3225=10%), the effect of trial allocation on T1 cancer distress was not significant: mean levels of cancer distress remained high and bordered on clinical levels regardless of trial allocation (153/326=47%). The effect of trial allocation on T1 general anxiety was not statistically significant. Significantly higher log transformed depression scores were found in the control group, but the absolute difference was very small. When converted back to the original scale (0-21 range), depression scores were low and within the normal range for both control and intervention groups.Table 20: Short-term psychosocial outcomes of trial allocation Outcome (T1)Intervention M (95% CI)+Control M (95% CI)+Test statistic, p-value, effect size Cancer distress** in low scorers at T0 (n=2896)2.14 (2.13, 2.16)8.54 (8.44, 8.64)2.11 (2.10, 2.12)8.26 (8.16, 8.36)F(1, 2893)=14.92, p<.001, 2=.005Cancer distress** in high scorers at T0 (n=326)2.50 (2.46, 2.53)12.14 (11.73, 12.55)2.53 (2.50, 2.57)12.61 (12.15, 13.09)F(1, 323)=2.26, p=.13, 2=.007Anxiety** (n=3209)1.54 (1.51, 1.57)3.67 (3.54, 3.80)1.56 (1.53, 1.59)3.78 (3.64, 3.92)F(1, 3206)=1.23, p=.27, 2 <.001Depression** (n=3208)1.26 (1.23, 1.29)2.53 (2.42, 2.63)1.34 (1.31, 1.37)2.81 (2.70, 2.92)F(1, 3205)=14.36, p<.001, 2=.004+log transformed scores in normal text and original scale scores in italics (analyses performed using log transformed scores).**baseline measure of the respective variable was included as a covariate.A significantly greater proportion of control arm participants were not very satisfied with their decision to take part (66%, 953/1451) compared to those in the intervention arm (58%, 875/1499) (χ2(1)=16.7, p<.001). Sensitivity analyses showed small changes in F values but not in statistical significance levels, therefore further exclusions were not made. Hypothesis 2: Intervention arm participants with a positive baseline CT scan result will report higher short-term cancer distress compared to those with negative resultsAs shown in Figure 15, 1653 participants who completed a T1 questionnaire were eligible for inclusion in the impact of baseline scan result analyses. Thirteen cases were excluded due to discrepancies in classification of their test result. Preliminary analyses were conducted to test for potential covariates and are shown in Appendix 7 Table 35. Baseline differences between the screening result groups were not significant, hence only the baseline scores of each dependent variable were included as covariates.Table 21 shows a statistically significant difference in T1 lung cancer distress among the different result groups. Participants who were positive for MDT referral (major lung abnormality) were significantly more distressed than each of the other result groups (negative mean difference = 0.36, p<.001; negative with incidental finding mean difference = 0.33, p<.001; positive for repeat scan mean difference = 0.24, p<.001). Lung cancer distress scores for the MDT group approached clinical thresholds, with upper confidence intervals crossing into clinically significant levels. Participants who were positive for a repeat scan also reported significantly greater T1 cancer distress than those receiving a negative (normal) result (mean difference = 0.12, p<.001). Differences in T1 general anxiety were found between groups. Those referred to MDT reported significantly greater anxiety than those receiving any other result (negative mean difference = 0.36, p<.001; incidental finding mean difference = 0.37, p=.022; positive for repeat scan mean difference = 0.31, p=.003), although their scores were in the low/normal range. The difference in anxiety between the MDT referral group and the incidental finding group was not significant when sensitivity analysis accounted for test timing issues. When participants who had completed their T1 questionnaires more than a month after they had been sent their result were excluded (n=174), the difference in anxiety for these groups was no longer significant (mean difference = 0.28, p=.18). Differences in depression scores were not statistically significant for any of the screening result groups. There was a significant association between screening result group and decision satisfaction (Table 22). A greater proportion of screening participants who were positive for MDT referral were very satisfied with their decision compared to the other three groups. Those who were positive for repeat scan had the greatest proportion not very satisfied with their decision. Table 21: Short-term psychosocial outcomes of baseline screening results Outcome (T1)Negative (n=763)+ ?M (95% CI)Negative with incidental finding (n=41)+ ?M (95% CI)Positive for repeat scan (n=788)+ ?M (95% CI)Positive for MDT referral (n=48)+ ?M (95% CI)Test statistic, p-value, effect sizeCancer distress (n=1634)2.12 (2.10, 2.13)8.32 (8.18, 8.45)2.15 (2.08, 2.22)8.56 (7.97, 9.19)2.23 (2.22, 2.25)9.34 (9.19, 9.49) a2.47 (2.41, 2.54)11.88 (11.10, 12.72) abcF(3, 1629)= 55.94, p<.001, 2=.093Anxiety (n=1630)1.51 (1.47, 1.55)3.54 (3.35, 3.73)1.50 (1.32, 1.68)3.49 (2.75, 4.39)1.56 (1.52, 1.60)3.76 (3.57, 3.96)1.87 (1.70, 2.04)5.49 (4.48, 6.67) abc^F(3, 1625) = 5.79, p=.001, 2=.011Depression (n=1630)1.27 (1.23, 1.31)2.55 (2.41, 2.70)1.20 (1.02, 1.38)2.31 (1.76, 2.97)1.26 (1.22, 1.30)2.51 (2.37, 2.66)1.40 (1.24, 1.56)3.05 (2.44, 3.78)F(3, 1625) = 1.10, p=.35, 2=.002+log transformed scores in normal text and original scale scores in italics (analyses performed using log transformed scores). Estimated marginal means are presented.?Ns vary within each cell due to missing data.asignificantly greater than mean score for negative result. bsignificantly greater than mean score for negative with incidental finding. csignificantly greater than mean score for positive for repeat scan.^not significantly different to Negative/incidental finding group in sensitivity analysis (excluding those who completed follow-up questionnaires >1 month after their result letter).Table 22: Short-term decision satisfaction according to baseline screening resultNegative (n=702)n (%)Negative with incidental finding (n=39) n (%)Positive for repeat scan (n=705) n (%)Positive for MDT referral (n=42) n (%) Test statistic (p-value)Decision satisfaction (T1) Not very satisfied 378 (54)22 (56)450 (64)18 (43)χ2(3) = 18.8 (<.001)Very satisfied324 (46)17 (44)255 (36)24 (57)6.6.4 Long-term outcomes analysisData were available for 2855 trial participants for the long-term outcomes analysis (Figure 15). Demographic and psychosocial characteristics of the sample at T2 were very similar to those of the sample included in T1 outcome analyses (see Table 19 and Appendix 7 Table 36).Hypothesis 3: There will be no difference in long-term cancer distress between trial armsAnalyses of T2 cancer distress scores were split by high and low baseline scores due to differential short-term effects of trial allocation on these groups. These effects were not evident at long-term follow-up: in both T0 low and high scoring participants, the difference between trial arms in T2 cancer distress levels was not statistically significant (Table 23). Control group participants had significantly higher T2 anxiety and depression scores than those receiving the intervention; however, the absolute differences between trial arms were minimal and not of clinical significance. When converted to raw scores, all three measures for both trial arms were within the normal range.A significantly greater proportion of those in the control arm (74%, 883/1189) were not very satisfied with their decision to take part in screening compared with the intervention arm (60%, 855/1422, χ2(1) =58.2, p<.001).Table 23: Long-term psychosocial outcomes of trial allocation Outcome (T2)Intervention M (95% CI)+Control M (95% CI)+Test statistic++, p-value, effect size Cancer distress** in low scorers at T0 (n=2574)2.10 (2.09, 2.11)8.15 (8.05, 8.25)2.09 (2.08, 2.10)8.10 (7.99, 8.25)F(1, 2571)=0.51, p=.48, 2<.001Cancer distress** in high scorers at T0 (n=268)2.44 (2.39, 2.48)11.43 (10.93, 11.95)2.46 (2.41, 2.51)11.69 (11.11, 12.30)F(1, 265)=0.43, p=.51, 2=.002Anxiety** (n=2836)1.54 (1.51, 1.57)3.66 (3.52, 3.80)1.61 (1.58, 1.65)4.02 (3.86, 4.19)F(1, 2833)=11.50, p=.001, 2=.004Depression** (n=2834)1.33 (1.30, 1.36)2.77 (2.67, 2.89)1.39 (1.36, 1.42)3.01 (2.89, 3.14)F(1, 2831)=8.65, p=.003, 2=.003+log transformed scores in normal text and original scale scores in italics.++ test statistics calculated from log transformed scores (analyses performed using log transformed scores).**baseline measure of each psychosocial variable was included as a covariate to its T2 outcome.Hypothesis 4: There will be no difference in long-term cancer distress between screening outcome groupsThe impact of screening outcome was assessed at long-term follow up. Participants were excluded if they had no baseline scan, had a MDT referral but no lung cancer diagnosed, were awaiting their scan or results, or had results that were not classified (n=267). Figure 16 indicates the final number of participants in each T2 screening outcome group: 23 true positive (i.e. lung cancer diagnosed), 445 false positive, 740 true negative and 78 incidental screening results. Figure 16: Screening outcome groups at T2Note: participants were categorised into screening outcome groups based on screening history and outcome of the final CT scan (where relevant) at the time of T2 questionnaire completion.As shown in Table 37 (see Appendix 7), differences between T2 screening outcome groups on baseline measures were not observed. Differences between screening outcome groups in T2 cancer distress, anxiety and depression were not statistically significant. The raw scores of all psychosocial variables in all groups were observed to be within the normal range and not clinically relevant. Similarly, no differences were found in satisfaction with decision to take part in screening between the four groups (see Table 25).Table 24: Impact of screening outcome at long-term follow-upOutcome (T2)True Negative(n=740)+ ?M (95% CI)Incidental Finding (n=78)+ ?M (95% CI)False Positive(n=445)+ ?M (95% CI)True Positive (n=23)+ ?M (95% CI)Test statistic++, p-value, effect sizeCancer distress (n=1279)2.11 (2.09, 2.12)8.22 (8.09, 8.36)2.14 (2.09, 2.19)8.48 (8.06, 8.93) 2.14 (2.12, 2.16)8.51 (8.33, 8.70)2.20 (2.10, 2.30)9.01 (8.16, 9.96)F(3, 1274)= 2.88, p=.04, 2=.007Anxiety (n=1280)1.57 (1.53, 1.62)3.82 (3.61, 4.03)1.45 (1.32, 1.59)3.28 (2.74, 3.89)1.52 (1.47, 1.58)3.59 (3.34, 3.85)1.37 (1.13, 1.62)2.94 (2.08, 4.03)F(3, 1275) = 1.94, p=.12, 2=.005Depression (n=1278)1.34 (1.30, 1.39)2.84 (2.68, 3.00)1.22 (1.09, 1.35)2.38 (1.97, 2.85)1.38 (1.33, 1.44)2.98 (2.78, 3.20)1.26 (1.02, 1.49)2.52 (1.79, 3.44)F(3, 1273) = 2.04, p=.11, 2=.005+log transformed scores in normal text and original scale scores in italics (analyses performed using log transformed scores).?Ns vary due to missing data.++ test statistics calculated from log transformed scores. **baseline measure of each psychosocial variable was included as a covariate to its T2 outcome.Table 25: Long-term decision satisfaction in screening outcome groupsTrue Negative(n=678)?n (%)Incidental Finding(n=71)?n (%) False Positive(n=408)?n (%)True Positive(n=21)?n (%)Test statistic (p-value)Decision satisfaction (T2) Not very satisfied411 (61)39 (55)239 (59)6 (29)χ2(3) = 9.29 (.03)Very satisfied267 (39)32 (45)169 (41)15 (71)?Ns vary due to missing data.6.6.5 Secondary analysis - linear mixed effects modelling6.6.5.1 Impact of trial allocation As an extension to ANCOVA, a linear mixed effects multivariable regression model was fitted to examine the impact of trial allocation on cancer distress at both T1 and T2. The fixed effects included baseline cancer distress and trial allocation, and the random effects included a random intercept and slope. After adjustment for baseline cancer distress, the difference in logn mean distress between the intervention and control arm was 0.02 (95% CI -0.13 to 0.17, p=.42) (see Table 26). When back transformed to scores on the original distress scale, this equated to a relative increase of 2% for those in the intervention arm (95% CI - 12% to 19%). 6.6.5.2 Univariable analysis Low baseline cancer distress (< 12.5) The overall pattern of cancer distress across trial allocation groups was examined, adjusting for baseline score and independently for additional effect modifiers. No statistical difference was observed between the intervention and control arms in cancer distress after adjustment for any additional effect modifier (see Table 26). For example, after adjusting for gender, the logn mean difference in cancer distress between intervention and control was 0.02 (95% CI -0.13 to 0.16; p=.40). This equated to a relative increase in original cancer distress scores of 2% in the intervention group (95% CI - 12% to 17%) compared to the control group. Table 26: Univariable analyses of the difference in logn cancer distress between trial allocation groups for low baseline distress scorers, using a linear mixed effects model Logn difference in cancer distress (Intervention – Control)EstimateSEp-value95% CIEffect modifiers ????LowerUpperBase model (baseline cancer distress & trial allocation)0.020.012.42-0.130.17& Gender 0.020.011.40-0.130.16& Age group*0.020.012.43-0.140.17& Smoking status0.010.012.46-0.140.17& Site0.020.011.43-0.140.16& IMD0.010.012.46-0.140.16& Education group^ 0.020.014.34-0.160.21& Ethnic group0.020.012.43-0.140.17& Time since recruitment centre?0.020.013.41-0.120.15& Result groupNot Estimable & Marital status+0.020.013.43-0.140.18?& Experience of lung cancer0.020.012.40-0.140.17*Age groups: up to 65, 66-70, over 70^A substantial amount of data were missing or uninformative for the education variable.? Time since recruitment centre groups: < 3 months, 3-6 months, 6-12 months, >12 months+ Marital status: single, widowed, divorced, living together, married The difference in cancer distress between the category levels of each modifier are shown in Table 27. The following effect modifiers explained cancer distress (p<.001): age group, smoking status, trial site, socioeconomic group, marital status, gender, lung cancer experience, and screening outcome group. Younger participants (65 years and under) were more distressed than those over 70, equating to a back-transformed increase of 6% in raw cancer distress scores for those up to age 65 (95% CI 5% to 8%). Those aged 66-70 were also more distressed than those over 70. Higher cancer distress scores were found in current smokers compared to ex-smokers, participants recruited from the Liverpool area, those who were more deprived (quintiles 1-3 compared with quintile 5), single or divorced (compared to married) individuals, females, and those who had experience of lung cancer. Since only intervention group participants received a CT scan, trial allocation group was fully nested with the result group effect modifier. Those who needed a repeat scan or MDT referral reported higher cancer distress than those who received a negative result. Table 27: Cancer distress effect modifiers analysed over T1 and T2 using a linear mixed effects model for participants with low baseline cancer distressBaseline effect modifierLogn difference in cancer distress 95 % CI???EstimateSELowerUpperp-valueAge group Up to 65 - Over 700.060.0080.050.08<.001 66 to 70 - Over 700.040.0070.020.05<.001Smoking status Ex smoker - Current smoker -0.080.006-0.06-0.09<.001 Never smoked? - Current smoker-0.000.1650.32-0.33.99Site Liverpool Papworth0.070.0060.060.08<.001IMD Most deprived (Q1) - Most affluent (Q5) 0.090.0080.07 0.10<.001 Q2 - Q50.050.0110.030.07<.001 Median affluent (Q3) - Q50.030.0090.010.04.004 Q4 - Q50.020.009 0.000.04.029Education ^ Up to GCSE - Beyond GCSE0.010.0070.000.03.05Ethnic group White - Non white0.010.035-0.060.08.79Marital status Married - Single-0.040.013-0.02-0.07.001 Married - Living together0.010.013-0.020.04.41 Married - Widowed-0.030.011-0.00-0.05.03 Married - Divorced-0.050.010-0.03-0.07<.001Gender Female - Male0.040.0070.030.06<.001Time since attended recruitment centre 3 to 6 months - < 3 months-0.000.013-0.030.031.00 6 to 12 months - < 3 months -0.050.016-0.09-0.01.03 > 12 months - < 3 months -0.030.013-0.120.06.19Result group* Negative with Incidental finding - Negative 0.020.022-0.020.07.33 Positive for a repeat scan - Negative0.090.0090.070.11<.001 Positive for MDT referral - Negative - 0.200.0230.150.24<.001Experience of lung cancer Yes - No experience?0.050.0060.030.06<.001^A substantial amount of data were missing or uninformative for the education variable.? very small N (2 participants) in the never smoked group.*Result group data only appropriate/relevant for those in the intervention arm.High baseline cancer distress (>= 12.5)When the previous analyses were repeated in those with high baseline cancer distress scores, a similar pattern was observed to that found for low baseline cancer distress scorers (data not shown).6.6.5.3 Impact of trial allocation adjusting for baseline cancer distress and other scientifically important effect modifiers Low baseline cancer distress (< 12.5) Having fit the scientifically justified and preconceived effect modifiers, the impact of trial allocation was not statistically significant. The logn mean difference was 0.03 (95% CI -0.14 to 0.19, p=.33) (see Table 28, left panel). Of the other effect modifiers included in the model, deprivation was significantly associated with distress, with those in the two most deprived groups being more distressed than those in the least deprived group (p<.01). Age group (p≤.01) and smoking status (p<.001) also influenced cancer distress, with participants up to age 70 and current smokers (compared with ex-smokers) reporting higher cancer distress. Time since recruitment centre attendance was not significantly associated with distress.High baseline cancer distress (>= 12.5)The impact of trial allocation was not statistically significant in participants with high baseline cancer distress (logn(difference) = -0.04 (95% CI -0.09 to 0.02, p=.20) (see Table 29, left panel). Only age group was found to influence cancer distress, with those under 70 reporting more distress than those over 70 years (p<.001).6.6.5.4 Impact of trial allocation adjusting for baseline cancer distress and all other scientifically and statistically important effect modifiers Low baseline cancer distress (< 12.5)After fitting and adjusting for all terms in the model, the impact of trial allocation on cancer distress was not statistically significant (logn(difference) = 0.03 (95% CI -0.20 to 0.26, p=.39). A forward stepping model fitting approach to nested models showed the following to be statistically important effect modifiers associated with higher cancer distress: Liverpool recruitment site (p<.001), female gender (p=.006), and having prior experience of lung cancer (p<.001) (see Table 28, right panel). After adjustment for the other effect modifiers, neither marital status (p=.24) nor ethnicity (p=.90) were found to exhibit a statistically significant effect on cancer distress.Consistent with secondary analysis (2), the following scientifically justified effect modifiers were significant: age and smoking status. Current smokers reported higher distress than ex-smokers (p<.001), and participants aged up to 65 years reported higher distress than those over 70 years (p<.001). Including recruitment site in the model removed the effect of deprivation quintile on cancer distress. From this, it can be surmised that the initial association between cancer distress and deprivation was likely to be confounded by recruitment site. Table 38 (see Appendix 7) shows that while half (50%) of those recruited from Liverpool were in the most deprived group and the rest distributed throughout the other four quintiles, almost half (46%) of those recruited from the Papworth site were in the least deprived group and the rest distributed throughout the other four quintiles.High baseline cancer distress (>= 12.5)The same model that was found from the forward stepping process was fitted on the participants with high baseline cancer distress. There was no impact of trial allocation on cancer distress, after adjustment for the other explanatory variables (see Table 29 right panel). However, it should be noted that the study was not powered to detect any differences in this particular subgroup, which is likely to be underpowered due to the relatively small sample size and effect size. The only significant influence on cancer distress was age group, with those aged 70 and under reporting higher cancer distress than those aged over 70 years.As with the analyses for low baseline cancer distress scorers, the inclusion of recruitment site showed a trend (p=.02) towards higher cancer distress in participants from Liverpool compared to Papworth.Table 28: Multivariable analyses for cancer distress adjusted for important effect modifiers - low baseline distressMultivariable analysis adjusted for scientifically important effect modifiers only (secondary analysis 2)^Multivariable analysis adjusted for scientifically and statistically important effect modifiers (secondary analysis 3)?95% CI95% CI???EstimateSELowerUpperp-value?EstimateSELowerUpperp-valueAllocation group (Intervention - Control)0.030.017-0.140.19.360.030.018-0.200.26.39Deprivation quintiles Most deprived (Q1) - Most affluent (Q5) 0.070.0100.050.09<.0010.020.012-0.010.04.13 Q2 - Q50.030.0120.010.06.0050.010.012-0.020.03.66 Median deprived (Q3) - Q50.020.0100.000.04.02-0.000.010-0.020.02.89 Q4 - Q50.020.0100.000.04.050.010.010-0.010.03.59Smoking status Never smoked - Current smoker-0.040.158-0.350.27.79-0.030.157-0.340.27.83 Ex-smoker - Current smoker-0.060.007-0.07-0.04<.001-0.060.007-0.05-0.08<.001Age group Up to 65 - Over 700.050.0090.030.07<.0010.050.0090.030.07<.001 66 to 70 - Over 700.020.0090.010.04.010.020.0080.020.04.02Time since attended recruitment centre 3 to 6 months - < 3 months-0.020.015-0.050.01.23-0.030.0150.00-0.05.09 6 to 12 months - < 3 months -0.050.020-0.00-0.11.05-0.060.026-0.11-0.01.03 > 12 months - < 3 months-0.040.016-0.140.06.14-0.050.025-0.00-0.10.05Ln Baseline cancer distress0.5210.0160.490.59<.0010.5550.0130.530.58<.001Statistically important additional cancer distress effect modifiers?????????Site: Liverpool - Papworth - N/A0.060.0080.040.07<.001Experience of lung cancer: Yes – no - N/A0.030.0070.020.05<.001Gender: Female - Male? - N/A?0.020.0080.010.04.004^adjusting for: trial allocation, deprivation, smoking status, age group, time since attended recruitment centre and baseline (T0) cancer distress.? adjusting for: trial allocation, deprivation, smoking status, age group, time since attended recruitment centre, baseline (T0) cancer distress, recruitment site, experience of lung cancer and gender.Table 29: Multivariable analyses for cancer distress adjusted for important effect modifiers - high baseline cancer distressMultivariable analysis adjusted for scientifically important effect modifiers only (secondary analysis 2)^Multivariable analysis adjusted for scientifically and statistically important effect modifiers. (secondary analysis 3)?Cancer distress effect modifiers95% CI95% CI?EstimateSELowerUpperp-valueEstimateSELowerUpperp-valueAllocation group (Intervention – Control)-0.040.027-0.090.02.20-0.030.027-0.080.02.26Deprivation quintiles Most deprived (Q1) - Most affluent (Q5) 0.010.040-0.100.10.87-0.080.05-0.170.02.14 Q2 - Q50.000.054-0.100.11.95-0.060.058-0.170.06.34 Median deprived (Q3) – Q50.070.045-0.020.16.130.020.048-0.070.11.68 Q4 - Q50.060.042-0.020.14.160.030.043-0.060.11.49Smoking status Never smoked - Current smokerN / AN / A Ex-smoker - Current smoker-0.040.027-0.090.02.18-0.040.027-0.090.02.17Age Up to 65 - Over 700.160.0400.080.24<.0010.160.0410.080.24<.001 66 to 70 - Over 700.180.0380.100.25<.0010.170.0380.100.25<.001Time since attended recruitment centre 3 to 6 months - < 3 months0.000.054-0.100.11.94-0.020.054-0.120.09.77 6 to 12 months - < 3 months -0.120.066-0.250.01.07-0.110.065-0.240.02.08 > 12 months - < 3 months -0.050.028-0.110.00.07-0.050.028-0.110.00.06Ln Baseline cancer distress0.6690.1190.430.90<.001?0.6240.1190.3890.860<001Statistically important additional cancer distress effect modifiers?????????Site: Liverpool - Papworth - N/A0.090.036-0.16-0.02.02Lung cancer experience: Yes - no- N/A0.030.027-0.030.08.31Gender: Female - Male- N/A?0.030.028-0.020.09.22^adjusting for: trial allocation, deprivation, smoking status, age group, time since attended recruitment centre and baseline (T0) cancer distress.? adjusting for: trial allocation, deprivation, smoking status, age group, time since attended recruitment centre, baseline (T0) cancer distress, recruitment site, experience of lung cancer and gender.6.7 Discussion The purpose of this chapter was to examine the psychosocial impact of participation in UKLS lung screening, in order to inform the implementation of a potential lung cancer screening programme. The present results should be interpreted alongside evidence of feasibility and cost-effectiveness when weighing up the benefit to harm ratio of LDCT lung screening for high risk individuals in the UK.6.7.1 Impact of trial allocationShort-term negative consequences were observed in individuals allocated to receive LDCT screening, with higher cancer distress in the intervention arm at 2-4 weeks follow-up. However, this was a temporary effect that did not persist at long-term follow-up. Linear mixed effects modelling confirmed that, despite the notional increase in cancer distress for those in the intervention arm in the short-term (but not in the long-term), there was little evidence of a meaningful difference between trial allocation groups once effect modifiers had been taken into account. Similarly, both the NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gZGVuIEJlcmdoPC9BdXRob3I+PFllYXI+MjAxMTwv

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ADDIN EN.CITE.DATA 87 trials found minimal long-term psychosocial effects of allocation to LDCT screening or no screening control. Although UKLS participants who were assigned to the control group reported slightly higher long-term anxiety and depression, the absolute differences were again small and not clinically significant. The latter trend is likely to reflect the control group’s disappointment and frustration at having been identified as high risk but denied the intervention, supported by the finding that a greater proportion were less satisfied with their decision to participate compared to the intervention arm. This finding mirrors the Danish Lung Cancer Screening Trial in which control arm participants reported more negative consequences ADDIN EN.CITE <EndNote><Cite><Author>Aggestrup</Author><Year>2012</Year><RecNum>92</RecNum><DisplayText>(85)</DisplayText><record><rec-number>92</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">92</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Aggestrup, L. M.</author><author>Hestbech, M. S.</author><author>Siersma, V.</author><author>Pedersen, J. H.</author><author>Brodersen, J.</author></authors></contributors><auth-address>The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.</auth-address><titles><title>Psychosocial consequences of allocation to lung cancer screening: a randomised controlled trial</title><secondary-title>BMJ Open</secondary-title><alt-title>BMJ open</alt-title></titles><periodical><full-title>BMJ Open</full-title><abbr-1>BMJ open</abbr-1></periodical><alt-periodical><full-title>BMJ Open</full-title><abbr-1>BMJ open</abbr-1></alt-periodical><pages>e000663</pages><volume>2</volume><number>2</number><dates><year>2012</year></dates><isbn>2044-6055 (Electronic)</isbn><accession-num>22382119</accession-num><urls><related-urls><url> perhaps perceiving a missed opportunity to gain reassurance from screening. Overall, scores on measures of distress, anxiety and depression in the present trial were within the normal range in each arm and at each time point. For the minority of participants with pre-existing high cancer distress, short-term distress levels were high regardless of trial allocation. These individuals could be identified for additional psychosocial support during routine lung screening. ADDIN EN.CITE <EndNote><Cite><Author>Brain</Author><Year>2011</Year><RecNum>98</RecNum><DisplayText>(97)</DisplayText><record><rec-number>98</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">98</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Brain, K.</author><author>Parsons, E.</author><author>Bennett, P.</author><author>Cannings-John, R.</author><author>Hood, K.</author></authors></contributors><auth-address>Institute of Medical Genetics, Clinical Epidemiology Interdisciplinary Research Group, Cardiff University School of Medicine, Cardiff, UK. brainke@cardiff.ac.uk</auth-address><titles><title>The evolution of worry after breast cancer risk assessment: 6-year follow-up of the TRACE study cohort</title><secondary-title>Psychooncology</secondary-title><alt-title>Psycho-oncology</alt-title></titles><periodical><full-title>Psychooncology</full-title><abbr-1>Psycho-oncology</abbr-1></periodical><alt-periodical><full-title>Psychooncology</full-title><abbr-1>Psycho-oncology</abbr-1></alt-periodical><pages>984-91</pages><volume>20</volume><number>9</number><keywords><keyword>Adult</keyword><keyword>*Anxiety</keyword><keyword>Breast Neoplasms/genetics/*psychology</keyword><keyword>Female</keyword><keyword>Follow-Up Studies</keyword><keyword>Genetic Predisposition to Disease/*psychology</keyword><keyword>*Health Behavior</keyword><keyword>Humans</keyword><keyword>Mammography/*psychology</keyword><keyword>Middle Aged</keyword><keyword>Questionnaires</keyword><keyword>Risk Assessment</keyword><keyword>Risk Factors</keyword></keywords><dates><year>2011</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>1099-1611 (Electronic)&#xD;1057-9249 (Linking)</isbn><accession-num>20677331</accession-num><urls><related-urls><url> Individual difference variables that adversely influenced levels of cancer distress over time, regardless of trial allocation, included female gender, younger age (under 65 years), smoking, deprivation, and having prior experience of lung cancer. In addition, psychosocial outcomes were poorer in those recruited at the Liverpool site, over and above the effect of deprivation, suggesting that supportive interventions to improve the quality of care and minimise distress should be implemented alongside routine CT lung screening6.7.2 Impact of CT screening resultsThe initial impact of trial allocation largely reflects transient adverse effects of positive screening results, which disappeared at up to two years follow-up. Within the intervention arm, there was a significant graded effect of screening result on short-term lung cancer distress. Unsurprisingly, participants who were referred to MDT because of a suspected major lung abnormality on their baseline scan reported higher cancer distress than any other screening result group, with levels close to threshold scores. Participants who required a repeat scan reported higher distress than those receiving an immediate “all-clear” result. Differences in general anxiety (but not depression) were observed, with higher levels of short-term anxiety in participants referred to MDT. However, MDT group scores were within the normal range and they were also more at ease with their decision to take part in UKLS than any other screening outcome group. The latter finding has been reported in other screening evaluation studies, reflecting decision consolidation and the perception that further diagnostic tests have been carried out thoroughly and for personal benefit.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DbGVtZW50czwvQXV0aG9yPjxZZWFyPjIwMDg8L1llYXI+

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ADDIN EN.CITE.DATA 99 Adverse effects of screening result were not seen in the long-term, when the final outcome of the screening episode was examined, although Type II error cannot be discounted due to the relatively small number of participants (within the psychosocial sample) who eventually received a lung cancer diagnosis. Similarly, the absence of observed psychosocial effects in the small group of participants with incidental findings may reflect the heterogeneity of clinical diagnoses in this group. A number of participants were excluded from screening outcomes analyses, for example those referred to MDT but in whom lung cancer was not diagnosed; longer-term effects in these individuals are therefore unknown. The potential for distress caused by unfavourable or unexpected findings of CT lung screening should not be ruled out, and people who receive such results may benefit from further psychosocial support. On balance, however, the pattern of findings supports those of previous studiesPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UYXlsb3I8L0F1dGhvcj48WWVhcj4yMDA0PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 95, 96 and low perceived benefit among smokers ADDIN EN.CITE <EndNote><Cite><Author>Silvestri</Author><Year>2007</Year><RecNum>93</RecNum><DisplayText>(92)</DisplayText><record><rec-number>93</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">93</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Silvestri, G. A.</author><author>Nietert, P. J.</author><author>Zoller, J.</author><author>Carter, C.</author><author>Bradford, D.</author></authors></contributors><auth-address>Division of Pulmonary and Critical Care Medicine, Department of Medicine, Medical University of South Carolina, 96 Jonathan Lucas St, PO Box 250630, Charleston, SC, USA. silvestri@musc.edu</auth-address><titles><title>Attitudes towards screening for lung cancer among smokers and their non-smoking counterparts</title><secondary-title>Thorax</secondary-title><alt-title>Thorax</alt-title></titles><periodical><full-title>Thorax</full-title><abbr-1>Thorax</abbr-1></periodical><alt-periodical><full-title>Thorax</full-title><abbr-1>Thorax</abbr-1></alt-periodical><pages>126-30</pages><volume>62</volume><number>2</number><keywords><keyword>Adult</keyword><keyword>Aged</keyword><keyword>*Attitude to Health</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/*diagnosis/prevention &amp; control/psychology</keyword><keyword>Male</keyword><keyword>Mass Screening/*psychology</keyword><keyword>Middle Aged</keyword><keyword>Smoking/*psychology</keyword></keywords><dates><year>2007</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0040-6376 (Print)&#xD;0040-6376 (Linking)</isbn><accession-num>17101739</accession-num><urls><related-urls><url> Individuals from more affluent backgrounds may have a better understanding of the benefits of screening and face fewer barriers than those from poorer backgrounds. ADDIN EN.CITE <EndNote><Cite><Author>Von Wagner</Author><Year>2011</Year><RecNum>1061</RecNum><DisplayText>(101)</DisplayText><record><rec-number>1061</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1061</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Von Wagner, C.</author></authors></contributors><titles><title>Inequalitities in participation in an organized national colorectal cancer screening programme; results from the first 2.6 million invitations in England</title><secondary-title>Int J Epidemiol</secondary-title></titles><periodical><full-title>Int J Epidemiol</full-title><abbr-1>International journal of epidemiology</abbr-1></periodical><pages>712-8</pages><volume>40</volume><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote>101 Furthermore, these groups continued to be under-represented as the trial progressed, with greater attrition seen in women, smokers, less affluent individuals, those from the Liverpool area, and those with higher baseline psychosocial scores. The same groups were also vulnerable to experiencing higher cancer distress when they did take part in the trial. 6.8 Summary and recommendationsThe UKLS psychosocial evaluation indicates that short-term negative consequences of lung screening were modest and temporary. This pattern of psychosocial outcomes is likely to reassure clinicians and policymakers who are weighing up the benefits and harms of routine lung cancer screening for high risk groups in the UK. However, the present findings must be interpreted alongside evidence of feasibility, cost-effectiveness and reach. The psychosocial evaluation highlighted important sub-groups who were under-represented in the trial and in whom lung cancer risk is known to be higher. In the case of a national lung cancer screening programme, efforts to improve uptake must include strategies for engaging women and targeting high risk, hard to reach groups including the over 70s, smokers, and those from the poorest areas. This may involve targeted initiatives designed to raise awareness of lung cancer, such as the Be Clear on Cancer campaignPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Jcm9ubW9uZ2VyPC9BdXRob3I+PFllYXI+MjAxNTwvWWVh

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ADDIN EN.CITE.DATA 102 and adapted screening invitation materials to prepare people for informed participation. Increasing uptake and providing support for under-served groups will be key to implementing an equitable lung cancer screening programme in the UK. CHAPTER 7: Health Economics7.1IntroductionEconomic evaluation compares health technologies in terms of relative costs and relative outcomes, with a view to identifying which is superior. In general, a health technology is superior to another if it produces (i) the same or better outcomes at a lower cost, or (ii) better outcomes at the same cost, or (iii) better outcomes at a higher cost, assuming society believes that the enjoyment of better outcomes justifies the additional expenditure. From the economist’s perspective, it would be rational to replace an existing technology with a novel technology, were the latter to be found to meet any one of these criteria of cost effectiveness.The novel versus existing technologies being compared here are population screening versus symptomatic presentation, in relation to the detection and subsequent treatment of lung cancers. The economic rationale for such a comparison is as follows. ADDIN EN.CITE <EndNote><Cite><Author>Whynes</Author><Year>2004</Year><RecNum>100</RecNum><DisplayText>(103)</DisplayText><record><rec-number>100</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">100</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Whynes, D.K.</author></authors></contributors><titles><title>Economics of cancer screening programmes</title><secondary-title>Expert Review of Pharmacoeconomics and Outcomes Research</secondary-title></titles><periodical><full-title>Expert Review of Pharmacoeconomics and Outcomes Research</full-title></periodical><pages>61-69</pages><volume>4</volume><number>1</number><dates><year>2004</year></dates><urls></urls></record></Cite></EndNote>103 Cancer is a progressive disease, evolving through a sequence of stages. Treatment is initiated once the disease is detected and, for any particular treatment, patient prognosis is a negative function of the stage of progression at detection. Thus, the later the stage at which the disease is diagnosed, the worse is the patient’s prognosis. The likelihood of detection from symptomatic presentation is a positive function of progression, i.e. the earlier the stage, the less likely are symptoms to present. Assuming a testing procedure exists which can detect disease in an asymptomatic population, it follows that the administration of screening tests to such a population will identify tumours at earlier stages than would occur otherwise. Compared with un-screened patients, those with screen-detected cancers can receive earlier treatments and will achieve superior health outcomes (more years of life). However, these outcomes would be obtained only at the additional expense of both screening the population at risk and treating immediately all those cases identified by the test results.Demonstrating that cancer screening would be cost effective by means of a randomised controlled trial requires the estimation of (i) the net costs of screening over detection via symptomatic presentation amongst trial subjects, (ii) net benefits, in terms of additional life expectancy on the part of screened subjects, and (iii) the ratio of net benefits to net costs incurred. For acceptability, this ratio must be consistent with society’s criterion of acceptable value for money in securing health gains. The prevailing convention for acceptability in the UK’s public health care system is ?20-30,000 per quality-adjusted life year (QALY) gained. ADDIN EN.CITE <EndNote><Cite><Author>National Institute for Health and Clinical Excellence</Author><Year>2008</Year><RecNum>101</RecNum><DisplayText>(104)</DisplayText><record><rec-number>101</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">101</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>National Institute for Health and Clinical Excellence,</author></authors></contributors><titles><title>Guide to the methods of technology appraisal</title></titles><dates><year>2008</year></dates><pub-location>London</pub-location><publisher>NICE</publisher><urls></urls></record></Cite></EndNote>104If informed by observation alone, the complete economic evaluation of any cancer screening trial is necessarily long term. Control subjects must be followed to identify the costs associated with the symptomatic presentation and treatment of disease corresponding to those cases detected and treated immediately as a result of screening. The necessary observation time will be the lead time of the cancer in question, that is, by how many years screening advances diagnosis. A sufficient period of follow-up must elapse thereafter before Kaplan-Meier-type estimation of survival gains for screened versus control subjects can be undertaken. To observe a mortality benefit, the observation period must allow for the control group tumours corresponding to screen-detected cancers to advance to a symptomatic state (lead time) and thence to death (survival time). Longer periods of follow-up improve the accuracy of such estimates and may in any case be required to ensure significance if the survival effects prove to be small. By way of example, the Nottingham colorectal cancer screening trial screened patients over eight years and reported relative survival gains and cost effectiveness over an average of 16 years of follow-up. ADDIN EN.CITE <EndNote><Cite><Author>Whynes</Author><Year>2004</Year><RecNum>102</RecNum><DisplayText>(105)</DisplayText><record><rec-number>102</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">102</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Whynes, D.K.</author><author>on behalf of the Nottingham FOB Screening Trial,</author></authors></contributors><titles><title>Cost-effectiveness of screening for colorectal cancer: evidence from the Nottingham faecal occult blood trial</title><secondary-title>Journal of Medical Screening</secondary-title></titles><periodical><full-title>Journal of Medical Screening</full-title></periodical><pages>11-15</pages><volume>11</volume><dates><year>2004</year></dates><urls></urls></record></Cite></EndNote>105 It was able to report differences in all-cause survival at 28 years from first recruitment. ADDIN EN.CITE <EndNote><Cite><Author>Whynes</Author><Year>2010</Year><RecNum>103</RecNum><DisplayText>(106)</DisplayText><record><rec-number>103</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">103</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Whynes, D.K.</author><author>Mangham, C.M.</author><author>Balfour, T.W.</author><author>Scholefield, J.H.</author></authors></contributors><titles><title>Analysis of deaths occurring within the Nottingham trial of faecal occult blood screening for colorectal cancer</title><secondary-title>Gut</secondary-title></titles><periodical><full-title>Gut</full-title></periodical><pages>1088-1093</pages><volume>59</volume><dates><year>2010</year></dates><urls></urls></record></Cite></EndNote>1067.1.1UKLS evaluation modelThe UKLS trial was very much smaller and very much briefer than the cited colorectal trial. Although the original intention had been to recruit more than 30,000 subjects and to follow them for at least five years, ADDIN EN.CITE <EndNote><Cite><Author>Field</Author><Year>2013</Year><RecNum>117</RecNum><DisplayText>(107)</DisplayText><record><rec-number>117</rec-number><foreign-keys><key app="EN" db-id="p22ttrsdm9xz2jezf2kxpdsaz5a2tzx05rxt">117</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Field, J.K.</author><author>Hansell, D.M.</author><author>Duffy, S.W.</author><author>Baldwin, D.R.</author></authors></contributors><titles><title>CT screening for lung cancer: countdown to implementation</title><secondary-title>Lancet Oncology</secondary-title></titles><periodical><full-title>Lancet Oncology</full-title></periodical><pages>e591-600</pages><volume>14</volume><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>107 UKLS did not proceed beyond its pilot phase, whereby approximately 2,000 subjects were screened and observed over 12-15 months. This short follow-up period precludes adopting the conventional approach to trial evaluation, namely, the measurement of long-term costs and outcomes in both the test and the control arms, and the comparison thereof. Of necessity, the observational element of the economic evaluation was restricted to those events and findings which occurred within the active trial period. Observable costs which accrued in the active period were the costs of (i) screening the target population, (ii) re-screening or investigating patients with suspicious nodules according to the trial protocol, ADDIN EN.CITE <EndNote><Cite><Author>Baldwin</Author><Year>2011</Year><RecNum>115</RecNum><DisplayText>(108)</DisplayText><record><rec-number>115</rec-number><foreign-keys><key app="EN" db-id="p22ttrsdm9xz2jezf2kxpdsaz5a2tzx05rxt">115</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Baldwin, D.R.</author><author>Duffy, S.W.</author><author>Wald, N.J.</author><author>Page, R.</author><author>Hansell, D.M.</author><author>Field, J.K.</author></authors></contributors><titles><title>UK Lung Screen (UKLS) nodule management protocol: modelling of a single screen randomised controlled trial of low-dose CT screening for lung cancer</title><secondary-title>Thorax</secondary-title></titles><periodical><full-title>Thorax</full-title></periodical><pages>3.8-311</pages><volume>66</volume><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote>108 (iii) diagnostic work-up and treatment for the detected cancers. In that which follows, all costs are expressed in UK sterling at 2011-12 prices. Where costs originally denominated in currencies other than sterling are reported, values have been converted to 2011-12 sterling at purchasing power parity exchange rates.Whilst the numbers and type of abnormality detected as a result of screening were also observable, the possible consequences of detection were not. Any survival benefits would accrue far beyond the current (observation) period. Being vital to the evaluation, these benefits were therefore modelled on the basis of observational data from other contexts. We presumed that the survival benefits of screening would be confined to those patients in whom cancers had been detected, and that such benefits would constitute the only source of health gain in the screening programme. For each of the cancers detected, the patient’s health gain would equal the life years to be expected following screen-detection and treatment less the life years which the patient might expect as a result of treatment following eventual symptomatic presentation, assuming the patient had survived that long. In addition to future benefits, we modelled the future costs of the diagnostic work-up and treatment of the same cancers which would present eventually, had they not been screen-detected. These were to be offset against the current period costs of the screening programme.UKLS recorded incidental findings, principally cases of pneumonia, bronchiectasis, interstitial lung disease and severe emphysema. Such findings are regularly reported in CT screening studies. ADDIN EN.CITE <EndNote><Cite><Author>Humphrey</Author><Year>2013</Year><RecNum>104</RecNum><DisplayText>(109)</DisplayText><record><rec-number>104</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">104</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Humphrey, L.L.</author><author>Deffebach, M.</author><author>Pappas, M.</author><author>Baumann, C.</author><author>Artis, K.</author><author>Mitchell, J.P.</author><author>Zakher, B.</author><author>Fu, R.</author><author>Slatore, C.G.</author></authors></contributors><titles><title>Screening for lung cancer with low-dose computed tomography: a systematic review to update the U.S. Preventive Services Task Force recommendation</title><secondary-title>Annals of Internal Medicine</secondary-title></titles><periodical><full-title>Annals of Internal Medicine</full-title></periodical><pages>411-420</pages><volume>159</volume><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>109 As the collection of cost and outcome data for individual non-cancer cases lay outside the trial protocol, the consequences of incidental findings have not been included in the UKLS economic evaluation. We note only that the effect of including costs and consequences of incidental findings would be indeterminate. Whilst it is probable that incidental findings would generate additional workup and imaging costs – studies in both Canada ADDIN EN.CITE <EndNote><Cite><Author>Kucharczyk</Author><Year>2011</Year><RecNum>105</RecNum><DisplayText>(110)</DisplayText><record><rec-number>105</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">105</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kucharczyk, M.J.</author><author>Menezes, R.J.</author><author>McGregor, A.</author><author>Paul, N.S.</author><author>Roberts, H.C.</author></authors></contributors><titles><title>Assessing the impact of incidental findings in a lung cancer screening study by using low-dose computed tomography</title><secondary-title>Canadian Association of Radiologists Journal</secondary-title></titles><periodical><full-title>Canadian Association of Radiologists Journal</full-title></periodical><pages>141-145</pages><volume>62</volume><number>2</number><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote>110 and Italy ADDIN EN.CITE <EndNote><Cite><Author>Priola</Author><Year>2013</Year><RecNum>106</RecNum><DisplayText>(111)</DisplayText><record><rec-number>106</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">106</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Priola, A.M.</author><author>Priola, S.M.</author><author>Giaj-Levra, M.</author><author>Basso, E.</author><author>Veltri, A.</author><author>Fava, C.</author><author>Cardinale, L.</author></authors></contributors><titles><title>Clinical implications and added costs of incidental findings in an early detection study of lung cancer by using low-dose spiral computed tomography</title><secondary-title>Clinical Lung Cancer</secondary-title></titles><periodical><full-title>Clinical Lung Cancer</full-title></periodical><pages>139-148</pages><volume>14</volume><number>2</number><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>111 have reported costs equivalent to around ?9 per participant – it is equally likely that the earlier diagnosis of these non-cancer conditions could potentially generate compensating outcome gains.7.2Current costsCosts were estimated from the NHS perspective. Cost-incurring events associated with the initial screens, and with ensuing work-up consequent upon the findings, were observed within the trial period. As per protocol, the number and frequency of scanning events depended upon test results, and these were as follows. (The figures presented here are those from an interim analysis, in November 2013; there are therefore small differences between these figures and the more recent ones reported in Chapter 5). Nine hundred and seventy nine CT scans revealed no nodules of concern (Category 1) and these subjects thereafter exited the trial. Four hundred and seventy nine CT scans revealed Category 2 nodules, which required repeat scans at 12 months. Four hundred and seventy two CT scans revealed Category 3 nodules which required repeat scans at three months. Of these subjects, 19 required an additional scan at around six months. Three hundred and ninety eight of these subjects were re-scanned at 12 months. Finally, 64 CT scans yielded results of concern sufficient for subjects to be referred directly to the MDT (Category 4). It follows that the execution of the UKLS protocol necessitated 3,363 CT scans in total. The vast majority of these were, of course, negative for lung cancer, either at the initial scan or at subsequent scans which served to eliminate false positives nominally detected in the earlier scans.As a result of the CT findings, a total of 114 cases were referred to the MDT for work-up, i.e. further investigation and management decisions. Between them, the MDTs thereafter initiated 122 further CT scans with contrast, 20 guided needle biopsies, 50 positron emission tomography (PET) scans and 4 endobronchial ultrasound biopsies (EBUS). With respect to treatment of the 42 cancers detected, we recorded 35 cases of surgery (primarily lobectomy and video-assisted thorascoscopic surgery (VATS)), either alone or in combination with radiotherapy (n=5) and/or chemotherapy (n =11). Four patients had surgical biopsies or resections for benign disease and two patients were referred for palliative care.The gross cost of events within the trial was estimated by combining the resource use audit above with unit costs of each event type. For most events, the unit costs of procedures were 2011-12 National Health Service tariffs or reference costs, as classified according to the appropriate Healthcare Resource Groups (HRG) coding. These unit costs derive from the financial returns from all relevant providers. Table 30 lists the unit costs of the events noted above, as a mean for the health care system and as the extremes of the inter-quartile range across producers. As costs specific to individual types of surgery are unavailable, all surgery cases were considered as “complex thoracic procedures, with critical care as necessary”. Likewise, the surgical biopsies were classified as “major thoracic procedures”. Three procedures which played only a minor role in the UKLS event audit had no assigned HRG per se (see Table 30). For these, we used updated unit costs which had been constructed by earlier researchers contributing to practice guidelines. ADDIN EN.CITE <EndNote><Cite><Author>National Collaborating Centre for Cancer</Author><Year>2011</Year><RecNum>107</RecNum><DisplayText>(112)</DisplayText><record><rec-number>107</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">107</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>National Collaborating Centre for Cancer,</author></authors></contributors><titles><title>The diagnosis and treatment of lung cancer (update of NICE clinical guideline 24)</title></titles><dates><year>2011</year></dates><pub-location>Cardiff</pub-location><publisher>NCCC</publisher><urls></urls></record></Cite></EndNote>112The mean gross cost of the UKLS protocol as implemented equals ∑CiNi across all events, where Ci = unit cost of, and Ni = number of occurrences of, event i. Given UKLS events and unit costs, the mean gross current cost amounted to ?687,617. This total comprised (i) ?282,490 for CT scans (ii) ?72,592 for work-up via the MDTs (iii) ?332,534 for treatments of the detected cancers. To construct confidence intervals we assumed normality in all unit costs and converted the inter-quartile ranges for the eight principal event types (see Table 30) into standard deviations. We then re-estimated the gross cost calculation by simulation, using distributions governed by standard deviations about the mean unit costs (Palisade @RISK with 20,000 iterations). The simulation produced a 95% confidence interval of ?479,173 to ?899,794.7.2.1Invitation and selectionOur calculations thus far have excluded the costs of selecting and inviting screening subjects. Obviously, subjects were invited to UKLS, but the sequential invitation procedure was devised to target and recruit particular participants to a clinical trial, and to obtain both clinical and socio-demographic data. An invitation protocol addressing the needs of research does not provide a template for a pure screening protocol: including the actual recruitment costs for UKLS would therefore prove misleading when judging the likely cost effectiveness of a screening programme. Were CT screening for lung cancer to be implemented in the UK it seems probable that it would be organised in a fashion similar to other national cancer screening programmes i.e. via a centralised, computerised, call system. We accordingly constructed a hypothetical recruitment process to represent the invitation of the UKLS subjects actually screened.We took our lead from UK colorectal screening, where the current cost of inviting subjects to flexible sigmoidoscopy screening has been estimated at around ?6 per invitation. ADDIN EN.CITE <EndNote><Cite><Author>Whyte</Author><Year>2011</Year><RecNum>108</RecNum><DisplayText>(113)</DisplayText><record><rec-number>108</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">108</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Whyte, S.</author><author>Chilcott, J.</author><author>Cooper, K.</author><author>Essat, M.</author><author>Stevens, J.</author><author>Wong, R.</author><author>Kalita, N.</author></authors></contributors><titles><title>Re-appraisal of the options for colorectal cancer screening: report for the NHS Bowel Cancer Screening Programme</title></titles><dates><year>2011</year></dates><pub-location>Sheffield</pub-location><publisher>School of Health and Related Research (ScHARR)</publisher><urls></urls></record></Cite></EndNote>113 A programme of CT colonography in the Netherlands has been the subject of a very detailed audit ADDIN EN.CITE <EndNote><Cite><Author>de Haan</Author><Year>2013</Year><RecNum>109</RecNum><DisplayText>(114)</DisplayText><record><rec-number>109</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">109</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>de Haan, M.C.</author><author>Thomeer, M.</author><author>Stoker, J.</author><author>Dekker, E.</author><author>Kuipers, E.J.</author><author>van Ballegooijen, M.</author></authors></contributors><titles><title>Unit costs in population-based colorectal cancer screening using CT colonography performed in university hospitals in The Netherlands</title><secondary-title>European Radiology</secondary-title></titles><periodical><full-title>Eur Radiol</full-title><abbr-1>European radiology</abbr-1></periodical><pages>897-907</pages><volume>23</volume><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>114 and the combined costs of invitation, reminders and distribution of results convert to a similar figure, around ?5.50 per person invited. An additional requirement for lung cancer would be the restriction of potential subjects to a defined high-risk category, as cost effectiveness is positively associated with disease prevalence. ADDIN EN.CITE <EndNote><Cite><Author>Whynes</Author><Year>2008</Year><RecNum>92</RecNum><DisplayText>(115)</DisplayText><record><rec-number>92</rec-number><foreign-keys><key app="EN" db-id="p22ttrsdm9xz2jezf2kxpdsaz5a2tzx05rxt">92</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Whynes, D.K.</author></authors></contributors><titles><title>Could CT screening for lung cancer ever be cost effective in the United Kingdom ?</title><secondary-title>Cost Effectiveness and Resource Allocation</secondary-title></titles><periodical><full-title>Cost Effectiveness and Resource Allocation</full-title></periodical><pages>5</pages><volume>6</volume><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>115 Risk profiling following individual person-to-person contact is likely to prove expensive, but the presence of many propriety risk calculators freely available via the Internet suggests the possibility of developing a cheaper alternative to judge eligibility. In our model, we assumed that pure invitation and selection costs would have amounted to ?10 per person invited. We assumed that, of those invited and selected, only 30% would agree thereafter to be screened. In other words, nearly 7,000 individuals would have to be approached in an actual programme to achieve the numbers screened in the trial. This proportion is similar to that of those expressing an interest in screening following receipt of the initial UKLS questionnaire. It should be noted, however, that real-world cancer screening typically achieves a higher uptake rate when implemented as a formal programme. The uptake rate for the initial phases of the national colorectal screening programme, for example, was of the order of 50%. ADDIN EN.CITE <EndNote><Cite><Author>Logan</Author><Year>2012</Year><RecNum>110</RecNum><DisplayText>(116)</DisplayText><record><rec-number>110</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">110</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Logan, R.F.A.</author><author>Patnick, J.</author><author>Nickerson, C.</author><author>Coleman, L.</author><author>Rutter, M.D.</author><author>von Wagner, C.</author><author>on behalf of the English Bowel Cancer Screening Evaluation Committee,</author></authors></contributors><titles><title>Outcomes of the Bowel Cancer Screening Programme (BCSP) in England after the first 1 million tests</title><secondary-title>Gut</secondary-title></titles><periodical><full-title>Gut</full-title></periodical><pages>1439-1446</pages><volume>61</volume><number>10</number><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>116 Factoring these invitation/selection costs into the total caused gross current costs to increase by ?67,260, or 10%, to ?754,877 (?544,824 to ?966,304).7.3Future benefitsTo predict life year gains consequent upon screen detection we adapted an existing survival model based on life tables. ADDIN EN.CITE <EndNote><Cite><Author>Whynes</Author><Year>2008</Year><RecNum>92</RecNum><DisplayText>(115)</DisplayText><record><rec-number>92</rec-number><foreign-keys><key app="EN" db-id="p22ttrsdm9xz2jezf2kxpdsaz5a2tzx05rxt">92</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Whynes, D.K.</author></authors></contributors><titles><title>Could CT screening for lung cancer ever be cost effective in the United Kingdom ?</title><secondary-title>Cost Effectiveness and Resource Allocation</secondary-title></titles><periodical><full-title>Cost Effectiveness and Resource Allocation</full-title></periodical><pages>5</pages><volume>6</volume><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>115 In brief, the model functions as follows. Life tables display general population mortality rates and survival rates, calculated for all causes of death. The mortality rate increases with age and life table data can be displayed as a survival curve, which maps the expected number of individuals from a starting cohort which survive at any given age. To estimate an individual's life expectancy at any age, T, we calculate the number of "years alive" in the cohort at every age. Summing these "years alive" from T to the oldest possible age equals the total number of years lived by individuals from age T. In effect, this is the area under the survival curve. The expectation of a single life at age T is obtained by dividing the total number of years lived by the number alive at age T.Onto the survival curve for the general population we superimposed two cancer-related survival curves. First, a cohort whose members are destined to develop lung cancer will, prior to presentation, decline in numbers at the same rate as in the general population cohort. Following symptomatic presentation, the majority will die within a few years following treatment, whilst the mortality rates for survivors will revert to those of the general cohort. Second, screening an identical cohort would lead to the treatment of the cancers at an earlier age. Unless earlier intervention entails no mortality risk, the relative decline in numbers in this cohort will be initially greater than the relative decline of those in either a general population or a pre-symptomatic cancer cohort. However, assuming earlier detection offers improved longer-term survival, the relative decline will be lower than for the symptomatic cohort at higher ages. Each of these cancer-related survival curves yields a life expectancy at age T, and difference between the two expectancies is the gain from screening.Modelling survival in a population screened at a particular age, TCT, therefore requires the specification of parameters. Firstly, we require cancer-specific mortality rates following screen detection, in order to modify population mortality rates. The observed pattern of initially-high mortality following diagnosis, quickly tapering off, suggests a negative exponential formulation. Mortality rate from age TCT equals A*(T–TCT)–B, where (T–TCT) =1,2,3… and A and B are parameters. The specified rates are applied at ages beyond T, up to the age at which the population mortality rate exceeds the specified rate; thereafter, the rate following detection defaults to the population rate. Secondly, we require an estimate of lead time (TCT – TS), that is, the years elapsing between a cancer being screen-detected at age TCT and the same cancer, assuming it had been un-detected, presenting symptomatically at age TS. Thirdly, we require cancer-specific mortality rates following symptomatic detection, in order to modify population mortality rates at ages beyond TS. We employ an equivalent formulation for mortality rate adjustment from age TS, i.e. C*(T–TS)–D, where (T–TS) =1,2,3… and C and D are parameters.7.3.1Age-sex-stage-specific survivalThe original, general, model ADDIN EN.CITE <EndNote><Cite><Author>Whynes</Author><Year>2008</Year><RecNum>92</RecNum><DisplayText>(115)</DisplayText><record><rec-number>92</rec-number><foreign-keys><key app="EN" db-id="p22ttrsdm9xz2jezf2kxpdsaz5a2tzx05rxt">92</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Whynes, D.K.</author></authors></contributors><titles><title>Could CT screening for lung cancer ever be cost effective in the United Kingdom ?</title><secondary-title>Cost Effectiveness and Resource Allocation</secondary-title></titles><periodical><full-title>Cost Effectiveness and Resource Allocation</full-title></periodical><pages>5</pages><volume>6</volume><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>115 failed to distinguish different cancer stages or different ages at screen-detection. The detailed findings of UKLS, however, permit a more rigorous specification and enable us to simulate screen-detecting each of those cancers actually detected. We solved the survival model for each cancer individually, using life tables specific to the patient’s sex and his/her age at screening, assuming post-detection survival rates specific to the cancer stage at detection. As noted above, this required the assumption of lead times and two sets of mortality rate adjustments (screen-detection vs. symptomatic presentation) for each individual cancer detected.UKLS detected ten cancers amongst women and 32 amongst men, at ages ranging from 56 to 77 years. The numbers by stage were 28, 8, 3 and 3 for stages 1 through 4, respectively. Survival rates by stage and sex were assigned by assumption, informed by the literature, as follows.The data available on which to build our survival assumptions were limited, because only the earliest of the clinical studies have accumulated sufficient follow-up time. With respect to post-treatment survival of screen-detected cancers, the principal source remains the US Early Lung Cancer Action Programme (ELCAP), which reported 92% 10-year survival for patients in which operable stage 1 tumours were identified, and 80% survival for all cancers detected and treated. ADDIN EN.CITE <EndNote><Cite><Author>International Early Lung Cancer Action Program Investigators</Author><Year>2006</Year><RecNum>111</RecNum><DisplayText>(117)</DisplayText><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">111</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>International Early Lung Cancer Action Program Investigators,</author></authors></contributors><titles><title>Survival of patients with stage 1 lung cancer detected on CT screening</title><secondary-title>New England Journal of Medicine</secondary-title></titles><periodical><full-title>New England Journal of Medicine</full-title></periodical><pages>1763-1771</pages><volume>355</volume><number>17</number><dates><year>2006</year></dates><urls></urls></record></Cite></EndNote>117 A Japanese programme ADDIN EN.CITE <EndNote><Cite><Author>Nawa</Author><Year>2012</Year><RecNum>112</RecNum><DisplayText>(118)</DisplayText><record><rec-number>112</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">112</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Nawa, T.</author><author>Nakagawa, T.</author><author>Mizoue, T.</author><author>Kusano, S.</author><author>Chonan, T.</author><author>Fukai, S.</author><author>Endo, K.</author></authors></contributors><titles><title>Long-term prognosis of patients with lung cancer detected on low-dose chest computed tomography screening</title><secondary-title>Lung Cancer</secondary-title></titles><periodical><full-title>Lung Cancer</full-title></periodical><pages>197-202</pages><volume>75</volume><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>118 reported 90% 5-year survival for all screen-detected cancers, with 97% for those detected at stage 1. An earlier mobile programme in Japan ADDIN EN.CITE <EndNote><Cite><Author>Sone</Author><Year>2007</Year><RecNum>113</RecNum><DisplayText>(119)</DisplayText><record><rec-number>113</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">113</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sone, S.</author><author>Nakayama, T.</author><author>Honda, T.</author><author>Tsushima, K.</author><author>Li, F.</author><author>Haniuda, M.</author><author>Takahashi, Y.</author><author>Suzuki, T.</author><author>Yamanda, T.</author><author>Kondo, R.</author><author>Hanaoka, T.</author><author>Takayama, F.</author><author>Kubo, K.</author><author>Fushimi, H.</author></authors></contributors><titles><title>Long-term follow-up study of a population-based 1996-1998 mass screening programme for lung cancer using mobile low-dose spiral computed tomography</title><secondary-title>Lung Cancer</secondary-title></titles><periodical><full-title>Lung Cancer</full-title></periodical><pages>329-341</pages><volume>58</volume><dates><year>2007</year></dates><urls></urls></record></Cite></EndNote>119 reported 10-year survival at 88% for stage 1 and 50% for cancers at more advanced stages. Survival projections from on-going trials have suggested 90% 2-year survival for cancers detected at early stages (1 and 2) and 47% for cancers at advanced stages. ADDIN EN.CITE <EndNote><Cite><Author>Bach</Author><Year>2007</Year><RecNum>114</RecNum><DisplayText>(120)</DisplayText><record><rec-number>114</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">114</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bach, P.B.</author><author>Jett, J.R.</author><author>Pastroino, U.</author><author>Tockman, M.S.</author><author>Swensen, S.J.</author><author>Begg, C.B.</author></authors></contributors><titles><title>Computed tomography screening and lung cancer outcomes</title><secondary-title>Journal of the American Medical Association</secondary-title></titles><periodical><full-title>Journal of the American Medical Association</full-title></periodical><pages>953-961</pages><number>297</number><dates><year>2007</year></dates><urls></urls></record></Cite></EndNote>120 Although not trial-based, audit studies from Alabama, USA ADDIN EN.CITE <EndNote><Cite><Author>Cerfolio</Author><Year>2009</Year><RecNum>115</RecNum><DisplayText>(121)</DisplayText><record><rec-number>115</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">115</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cerfolio, R.J.</author><author>Bryant, A.S.</author></authors></contributors><titles><title>Survival of patients with true pathologic stage 1 non-small cell lung cancer</title><secondary-title>Annals of Thoracic Surgery</secondary-title></titles><periodical><full-title>Annals of Thoracic Surgery</full-title></periodical><pages>917-923</pages><volume>88</volume><dates><year>2009</year></dates><urls></urls></record></Cite></EndNote>121 and from Taipei, Taiwan ADDIN EN.CITE <EndNote><Cite><Author>Hung</Author><Year>2012</Year><RecNum>116</RecNum><DisplayText>(122)</DisplayText><record><rec-number>116</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">116</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Hung, J.J.</author><author>Jeng, W.J.</author><author>Hsu, W.H.</author><author>Huang, B.S.</author><author>Wu, Y.C.</author></authors></contributors><titles><title>Time trends of overall survival and survival after recurrence in completely resected stage I non-small cell lung cancer</title><secondary-title>Journal of Thoracic Oncology</secondary-title></titles><periodical><full-title>Journal of Thoracic Oncology</full-title></periodical><pages>397-405</pages><volume>7</volume><number>2</number><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>122 indicated five-year survival above 70% for those treated for stage 1 cancers. As screening trials tend to focus on detecting early-stage disease, relatively little attention has been paid to the impact of screening on the survival of late-stage cancers. A recent review ADDIN EN.CITE <EndNote><Cite><Author>Provencio</Author><Year>2011</Year><RecNum>117</RecNum><DisplayText>(123)</DisplayText><record><rec-number>117</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">117</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Provencio, M.</author><author>Isla, D.</author><author>Sánchez, A.</author><author>Cantos, B.</author></authors></contributors><titles><title>Inoperable stage III non-small cell lung cancer: current treatment and role of vinorelbine</title><secondary-title>Journal of Thoracic Disease</secondary-title></titles><periodical><full-title>Journal of Thoracic Disease</full-title></periodical><pages>197-204</pages><volume>3</volume><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote>123 suggested that survival following treatable stage 3 cancer may be similar to that of early stage disease and, with appropriate therapy, incurable stage 3 cancer could produce up to 23% five-year survival. 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ADDIN EN.CITE.DATA 126, 127 as the basis for modelling post-presentation survival of the trial’s cancers, assuming they had not been screen-detected. We assumed that, were the patient to survive, a cancer which had been screen-detected at stage S would have eventually presented at stage S or later, i.e. cancers would possibly progress but would never regress. For example, a stage 1 cancer could eventually present at any of the 4 stages according to the UK distribution, but a stage 3 cancer could present only at stages 3 or 4. Table 31 displays the survival rates assumed, based on the literature cited, and the implied values for the parameters for the life table modifications described above. It should be noted that survival rates for women in the absence of screening are superior to those of men, although the absence of sex-specific data required us to assume the same screen-detection survival rates for both sexes.7.3.2Lead timeThe final assumption required for outcome estimation is the lead time of screening. Whilst some modellers have presumed that lung cancer develops sufficiently rapidly to produce lead times of around two years,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Hb2xkYmVyZzwvQXV0aG9yPjxZZWFyPjIwMTA8L1llYXI+

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ADDIN EN.CITE.DATA 128-130 those actually engaged in screening have been more circumspect. Development times of six and eight years between stages 1 (if screen-detected) and four (if presenting) have been suggested for Japan ADDIN EN.CITE <EndNote><Cite><Author>Soda</Author><Year>1999</Year><RecNum>125</RecNum><DisplayText>(131)</DisplayText><record><rec-number>125</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">125</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Soda, H.</author><author>Oka, M.</author><author>Tomita, H.</author><author>Nagashima, S.</author><author>Soda, M.</author><author>Kohno, S.</author></authors></contributors><titles><title>Length and lead time biases in radiologic screening for lung cancer</title><secondary-title>Respiration</secondary-title></titles><periodical><full-title>Respiration</full-title></periodical><pages>511-517</pages><volume>66</volume><dates><year>1999</year></dates><urls></urls></record></Cite></EndNote>131 and ELCAP, ADDIN EN.CITE <EndNote><Cite><Author>Henschke</Author><Year>2003</Year><RecNum>1047</RecNum><DisplayText>(132)</DisplayText><record><rec-number>1047</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1047</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Henschke, C.I.</author><author>Yankelevitz, D.F.</author><author>Kostis, W.J.</author></authors></contributors><titles><title>CT screening for lung cancer</title><secondary-title>Seminars in Ultrasound, CT, and MRI</secondary-title></titles><periodical><full-title>Seminars in Ultrasound, CT, and MRI</full-title></periodical><pages>23-32</pages><volume>24</volume><number>1</number><dates><year>2003</year></dates><urls></urls></record></Cite></EndNote>132 respectively, and these figures set the upper limits on lead times. A Danish CT trial has posted lead times of 4-5 years for early-stage cancers. ADDIN EN.CITE <EndNote><Cite><Author>Saghir</Author><Year>2012</Year><RecNum>1048</RecNum><DisplayText>(133)</DisplayText><record><rec-number>1048</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1048</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Saghir, Z.</author><author>Dirksen, A.</author><author>Ashraf, H.</author><author>Bach, K.S.</author><author>Clementsen, P.F.</author><author>D?ssing, M.</author><author>Hansen, H.</author><author>Kofoed, K.F.</author><author>Larsen, K.R.</author><author>Mortensen, J.</author><author>Rasmussen, J.F.</author><author>Seersholm, N.</author><author>Skov, B.G.</author><author>Thorsen, H.</author><author>T?nnesen, P.</author><author>Pedersen, J.H.</author></authors></contributors><titles><title>CT screening for lung cancer brings forward early disease. The randomised Danish Lung Cancer Screening Trial: status after five annual screening rounds with low-dose CT</title><secondary-title>Thorax</secondary-title></titles><periodical><full-title>Thorax</full-title><abbr-1>Thorax</abbr-1></periodical><pages>296-301</pages><volume>67</volume><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>133 Analysis of the US National Lung Screening Trial (NLST) data suggests the majority of, but by no means all, cancers would present within five years. ADDIN EN.CITE <EndNote><Cite><Author>Patz</Author><Year>in press</Year><RecNum>128</RecNum><DisplayText>(134)</DisplayText><record><rec-number>128</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">128</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Patz, E.F.</author><author>Pinsky, P.</author><author>Gatsonis, C.</author><author>Sicks, J.D.</author><author>Kramer, B.S.</author><author>Tammem?gi, M.C.</author><author>Chiles, C.</author><author>Black, W.C.</author><author>Aberle, D.R.</author><author>for the NLST Overdiagnosis Manuscript Writing Team,</author></authors></contributors><titles><title>Overdiagnosis in low-dose computed tomography screening for lung cancer</title><secondary-title>Journal of the American Medical Association</secondary-title></titles><periodical><full-title>Journal of the American Medical Association</full-title></periodical><dates><year>in press</year></dates><urls></urls></record></Cite></EndNote>134A possible approach to estimating lead time in the case of UKLS subjects would be to compare the mean subject ages at detection of the cancers by stage and the ages of symptomatic presentation currently observed in the UK. ADDIN EN.CITE <EndNote><Cite><Author>Walters</Author><Year>2013</Year><RecNum>120</RecNum><DisplayText>(126)</DisplayText><record><rec-number>120</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">120</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Walters, S.</author><author>Maringe, C.</author><author>Coleman, M.P.</author><author>Peake, M.D.</author><author>Butler, J.</author><author>Young, N.</author><author>Bergstr?m, S.</author><author>Hanna, L.</author><author>Jakobsen, E.</author><author>K?lbeck, K.</author><author>Sundstr?m, S.</author><author>Engholm, G.</author><author>Gavin, A.</author><author>Gjerstorff, M.L.</author><author>Hatcher, J.</author><author>Johannesen, T.B.</author><author>Linklater, K.M.</author><author>McGahan, C.E.</author><author>Steward, J.</author><author>Tracey, E.</author><author>Turner, D.</author><author>Richards, M.A.</author><author>Rachet, B.</author><author>for the ICBP Module 1 Working Group,</author></authors></contributors><titles><title>Lung cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK: a population-based study, 2004–2007</title><secondary-title>Thorax</secondary-title></titles><periodical><full-title>Thorax</full-title><abbr-1>Thorax</abbr-1></periodical><pages>551-564</pages><volume>68</volume><number>6</number><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>126 The integer age differences amount to three, two and one years, for stages 1 through 3 respectively. In other words, the stage 1 cancers in UKLS were detected, on average, at an age three years lower than cancers of stage 1 or later which present symptomatically. Whilst it is reasonable to expect lead times to fall by stage at detection, shorter lead times per se raise expected gains from screening in the survival model. To err on the side of caution, therefore, we assumed, first, the detection of stage 4 cancers would secure no survival advantage: stage 4 lead times, as it were, would correspond to the modest gains in earlier initiation of treatment. Second, lead times for stages 1 through 3 would be six, four and two years, respectively. Doubling our previous estimate ensured consistency with the more conservative published opinions. We note in passing that incorporating lead time in a life table model corrects for over-diagnosis, as it allows subjects to die of other causes before lead time elapses.As the survival gain for each individual cancer depends on stage, age and sex, each gain accrues at different times. For example, the model calculates that a 70-year old male with stage 1 cancer would have died at 78.1 years in the absence of screening but would, as a result of early detection, gain 4.1 further years of life beyond that age. A 70-year old male with stage 3 cancer would gain an extra 0.6 years of life beyond the age at which he would have died without screening, in 4.3 years’ time. To enable summation of survival gains accruing at different times we discounted future life year gains to present values at 3.5% annually.7.3.3Predicted outcomesThe model predicted total life year gains of 137.2 (discounted 89.4) from detecting and treating the 42 cancers. This translated to an average gain of 3.3 (CI 2.6 to 3.9) life years per cancer, undiscounted, and 2.1 (CI 1.7 to 2.5) life years, discounted. The average gain per person screened was 0.07 (discounted 0.05) life years, i.e. 25.1 (16.4) life days. Most of the total life year gains from screening (86% discounted) accrued as a result of the early detection and treatment of stage 1 cancers. It follows that model outcomes are especially sensitive to the parameters associated with stage 1 survival. Were we to assume a seven-year lead time for stage 1 cancers instead of six, total gains would be 111.8 (discounted 73.3) life years and the average gain per cancer would be 2.7 (CI 2.1 to 3.2) life years per cancer, undiscounted, and 1.7 (CI 1.4 to 2.1), discounted. The assumption of a five-year lead time increases total gains to 151.8 (discounted 98.7) life years, and average gains to 3.6 (CI 2.8 to 4.4) life years per cancer, undiscounted, and 2.4 (CI 1.9 to 2.8), discounted.These predicted survival gains appear comparable to that of a US state-transition simulation model ADDIN EN.CITE <EndNote><Cite><Author>McMahon</Author><Year>2008</Year><RecNum>129</RecNum><DisplayText>(135)</DisplayText><record><rec-number>129</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">129</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>McMahon, P.M.</author><author>Kong, C.Y.</author><author>Johnson, B.E.</author><author>Weinstein, M.C.</author><author>Weeks, J.C.</author><author>Kuntz, K.M.</author><author>Shepard, J-A. O.</author><author>Swensen, S.J.</author><author>Gazelle, G.S.</author></authors></contributors><titles><title>Estimating long-term effectiveness of lung cancer screening in the Mayo CT screening study</title><secondary-title>Radiology</secondary-title></titles><periodical><full-title>Radiology</full-title></periodical><pages>278-287</pages><volume>248</volume><number>1</number><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>135 which used national epidemiological and clinical data. ADDIN EN.CITE <EndNote><Cite><Author>Swensen</Author><Year>2005</Year><RecNum>1050</RecNum><DisplayText>(136)</DisplayText><record><rec-number>1050</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1050</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Swensen, S.J.</author><author>Jett, J.R.</author><author>Hartman, T.E.</author><author>Midthun, D.E.</author><author>Mandrekar, S.J.</author><author>Hillman, S.L.</author><author>Sykes, A-M.</author><author>Aughenbaugh, G.L.</author><author>Bungum, A.O.</author><author>Allen, K.L.</author></authors></contributors><titles><title>CT screening for lung cancer: five-year prospective experience</title><secondary-title>Radiology</secondary-title></titles><periodical><full-title>Radiology</full-title></periodical><pages>259-265</pages><volume>235</volume><dates><year>2005</year></dates><urls></urls></record></Cite></EndNote>136 The estimated survival gain from a single screen was 0.01 to 0.04 life years per person screened, depending upon the assumptions made. ADDIN EN.CITE <EndNote><Cite><Author>McMahon</Author><Year>2008</Year><RecNum>1044</RecNum><DisplayText>(137)</DisplayText><record><rec-number>1044</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1044</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>McMahon, P.M.</author><author>Kong, C.Y.</author><author>Weinstein, M.C.</author><author>Tramontano, A.C.</author><author>Cipriano, L.E.</author><author>Johnson, B.E.</author><author>Weeks, J.C.</author><author>Gazelle, G.S.</author></authors></contributors><titles><title>Adopting helical CT screening for lung cancer</title><secondary-title>Cancer</secondary-title></titles><periodical><full-title>Cancer</full-title></periodical><pages>3440-3449</pages><volume>113</volume><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>137 A more recent synthesis of five independent US models, again founded on the most recent clinical evidence, yielded predicted gains of between 0.02 to 0.09 life years per person screened, depending on assumptions relating to screening frequency and subjects’ smoking history. ADDIN EN.CITE <EndNote><Cite><Author>de Koning</Author><Year>(in press)</Year><RecNum>132</RecNum><DisplayText>(138)</DisplayText><record><rec-number>132</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">132</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>de Koning, H.J.</author><author>Meza, R.</author><author>Plevritis, S.K.</author><author>Ten Haaf, K.</author><author>Munshi, V.N.</author><author>Jeon, J.</author><author>Erdogan, S.A.</author><author>Kong, C.Y.</author><author>Han, S.S.</author><author>van Rosmalen, J.</author><author>Choi, S.E.</author><author>Pinsky, P.F.</author><author>de Gonzalez, A.B.</author><author>Berg, C.D.</author><author>Black, W.C.</author><author>Tammem?gi, M.C.</author><author>Hazelton, W.D.</author><author>Feuer, E.J.</author><author>McMahon, P.M.</author></authors></contributors><titles><title>Benefits and harms of computed tomography lung cancer screening strategies: a comparative modeling study for the U.S. Preventive Services Task Force</title><secondary-title>Annals of Internal Medicine</secondary-title></titles><periodical><full-title>Annals of Internal Medicine</full-title></periodical><dates><year>(in press)</year></dates><urls></urls></record></Cite></EndNote>1387.4Future costs avoidedThe eventual presentation of the UKLS cancers, had they not been detected and treated by screening, would have entailed costs of investigation and treatment following symptomatic presentation. The act of screening allowed these costs to be avoided, and it is appropriate to offset any such cost savings against the current screening-related costs of detection and treatment. We assumed that the treatment patterns following presentation of the potential cancers would have followed current practice. According to the National Lung Cancer Audit for 2012, 20% of all lung cancers presented symptomatically at stages 1 and 2, with the remainder at later stages. Of those presenting, 95% were managed through an MDT and received work-up. Thereafter, 15% of patients were treated surgically, 30% received radiotherapy and 57% received chemotherapy, although many patients actually received no treatment with curative intent. ADDIN EN.CITE <EndNote><Cite><Author>NLCA Project Team</Author><Year>2013</Year><RecNum>133</RecNum><DisplayText>(139)</DisplayText><record><rec-number>133</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">133</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>NLCA Project Team,</author></authors></contributors><titles><title>National Lung Cancer Audit Report 2013</title></titles><dates><year>2013</year></dates><pub-location>Leeds</pub-location><publisher>Health and Social Care Information Centre</publisher><urls></urls></record></Cite></EndNote>139Gross costs of management in this scenario were calculated using the same unit costs (see Table 30). We assumed that the work-up for cancers presenting symptomatically would be as for the screen-detected cancers, although PET-CT scans would be ordered only for those undergoing surgery. Following the assumed lead times, the costs of the early and late stage cancer presenting symptomatically would be incurred six and two years in the future, respectively. From the life tables, we calculated that 12% (4%) of patients with early (late) stage cancers would have died before presentation and treatment, thereby obviating the need for any expenditure on management. The gross cost avoided, that is, the cost of managing the cancers which would have presented symptomatically, amounted to ?213,658. This is 28% of the costs of management following screen detection and is equivalent, with discounting, to ?189,379 in current values (CI 152,740 to 230,643)7.5Cost-effectivenessThe incremental cost effectiveness ratio (ICER) of screen-detection, based on UKLS and relative to no screening, is the additional costs per expected life year gained. The mean net current cost of screen detection is the mean gross current cost (?754,877) minus the mean discounted costs avoided (?189,379), which amounts to ?565,498 (CI 361,102 to 767,672). Given a predicted gain in discounted life years of 89.4, the mean ICER amounts to ?6,325 per life year gained. Confidence intervals were constructed by simulation, using the estimated cost distribution for the ICER’s numerator. Given the uncertainty over lead time, outcomes were assumed to vary uniformly between 73.0 and 98.7 life years, the outcomes estimated assuming either seven or five year lead times, respectively. The simulation produced 95% confidence intervals for the ICER of ?4,109 to ?9,430.7.5.1Quality-adjusted life yearsAlthough the health-related quality of life (HRQL) of UKLS subjects was measured using the EQ-5D instrument prior to screening, an equivalent assessment post-screening was precluded by the time constraint. In order to translate life year gains into the more commonly-quoted quality-adjusted life year (QALY) gains, we followed the practice of other recent economic evaluations (see below). Most of the gains from a screening programme accrue to those treated successfully for early stage cancers, with the result that the HRQL pre- and post-screening must be essentially similar to population norms. For the UK, HRQL norms for the sexes/ages at which simulated deaths from cancer would have occurred lie in the range 0.71 to 0.78 relative to perfect health. ADDIN EN.CITE <EndNote><Cite><Author>Kind</Author><Year>1999</Year><RecNum>134</RecNum><DisplayText>(140)</DisplayText><record><rec-number>134</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">134</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Kind, P.</author><author>Hardman, G.</author><author>Macran, S.</author></authors></contributors><titles><title>UK population norms for EQ-5D (Discussion Paper 172)</title></titles><dates><year>1999</year></dates><pub-location>York</pub-location><publisher>Centre for Health Economics, University of York</publisher><urls></urls></record></Cite></EndNote>140 Adjusting life year gains for each cancer detected by the HRQL coefficient for the patient’s expected age at death transforms the predicted total gain of 89.4 life years into 66.8 QALYs, or 0.03 QALYs per person screened. The mean discounted gain per cancer detected was 1.6 QALYs. At 2.2 QALYs, expected mean gains for stage 1 cancers were considerably higher than QALY gains from detecting stage 2 cancers (0.6) and cancers at more advanced stages (0.2). With outcomes defined in terms of QALYs as opposed to life years, the ICER equalled ?8,466 per QALY gained (CI 5,516 to 12,634).7.5.2A modified protocolThe vast majority of cancers detected during the UKLS came via the category 3 or category 4 nodule management routes. Around 24% of trial subjects followed the category 2 route after their initial CT scan, yet that route yielded only one cancer. The question arises whether including the category 2 classification was, in itself, cost effective. Our calculations thus far enable us to infer what would had happened had (i) only category 3 and 4 subjects proceeded to further investigation and treatment, (ii) both category 1 and 2 subjects immediately exited the trial. Under this revised scenario, the UKLS protocol would have been less costly, at the expense of one cancer remaining undetected. Not re-screening the subjects classified as category 2, as the original protocol had required, would have saved ?49,816 in CT scan costs One fewer cancer detected would have avoided an MDT-directed work-up and treatment. Pro-rata savings would amount to ?1,728 for work-up and ?7,917 for treatment. The single cancer would have presented symptomatically, at an expected future cost of ?4,508, discounted. Therefore, the net savings from excluding the category 2 arm would have been ?54,953, making the net cost of the modified protocol ?510,545. We make the conservative assumption that the cancer not detected would have been at stage 1. We have already estimated the expected gain from detecting a stage 1 cancer at 2.2 QALYs, discounted, making the outcome of the modified protocol 64.6 QALYs. It follows that the ICER of the modified protocol would be ?7,903 per QALY gained.This revised ICER is marginally lower than the original baseline value, indicating that, as a result of the change in protocol, cost savings outweigh the reduction in outcome. The significance of this is best understood in reverse, by taking the modified protocol, rather than the original, as the reference case. The move from modified (no repeat CTs for Category 2 nodules) to original would entail the addition of ?54,953 in costs for an outcome gain of 2.2 QALYs, implying an ICER of ?24,978.7.5.2Comparison with other estimatesUntil around ten years ago, cost effectiveness evaluations of CT screening consisted of technically-sophisticated mathematical models, usually of the Markov stage transitions type. These models were based almost entirely on unsubstantiated assumptions, as was perhaps inevitable, given the paucity of clinical evidence at the time. The models were typically opaque, thereby frustrating attempts to discern reasons for differences in performance. It is probable, however, that differing assumptions (for example, about sojourn time, lead time, inter-stage transition probabilities, the effect of screening on subject’s cigarette smoking behaviour, and the probabilities of harm owing to repeated screening) accounted for the unhelpfully-wide range of ICERs predicted. ADDIN EN.CITE <EndNote><Cite><Author>Black</Author><Year>2006</Year><RecNum>135</RecNum><DisplayText>(141)</DisplayText><record><rec-number>135</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">135</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Black, C.</author><author>Bagust, A.</author><author>Boland, A.</author><author>Walker, S.</author><author>McLeod, C.</author><author>De Verteuil, R.</author><author>Ayres, J.</author><author>Bain, L.</author><author>Thomas, S.</author><author>Godden, D.</author><author>Waugh, N.</author></authors></contributors><titles><title>The clinical effectiveness and cost-effectiveness of computed tomography screening for lung cancer: systematic reviews</title><secondary-title>Health Technology Assessment</secondary-title></titles><periodical><full-title>Health Technology Assessment</full-title></periodical><volume>10</volume><number>3</number><dates><year>2006</year></dates><urls></urls></record></Cite></EndNote>141The publication of experimental data has enabled recent modellers to build on more robust foundations, yet the range of reported ICERs remains substantial. Consider just three examples. First, a US simulation ADDIN EN.CITE <EndNote><Cite><Author>McMahon</Author><Year>2011</Year><RecNum>1045</RecNum><DisplayText>(142)</DisplayText><record><rec-number>1045</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">1045</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>McMahon, P.M.</author><author>Kong, C.Y.</author><author>Bouzan, C.</author><author> Weinstein, M.C.</author><author>Cipriano, L.</author><author>Tramontano, A.C.</author><author>Johnson, B.E.</author><author>Weeks, J.C.</author><author>Gazelle, G.S.</author></authors></contributors><titles><title>Cost-effectiveness of CT screening for lung cancer in the US</title><secondary-title>Journal of Thoracic Oncology</secondary-title></titles><periodical><full-title>Journal of Thoracic Oncology</full-title></periodical><pages>1841-1848</pages><volume>6</volume><number>11</number><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote>142 based on the state-transition model informed by national epidemiological and Mayo Clinic CT trial data ADDIN EN.CITE <EndNote><Cite><Author>McMahon</Author><Year>2008</Year><RecNum>129</RecNum><DisplayText>(135)</DisplayText><record><rec-number>129</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">129</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>McMahon, P.M.</author><author>Kong, C.Y.</author><author>Johnson, B.E.</author><author>Weinstein, M.C.</author><author>Weeks, J.C.</author><author>Kuntz, K.M.</author><author>Shepard, J-A. O.</author><author>Swensen, S.J.</author><author>Gazelle, G.S.</author></authors></contributors><titles><title>Estimating long-term effectiveness of lung cancer screening in the Mayo CT screening study</title><secondary-title>Radiology</secondary-title></titles><periodical><full-title>Radiology</full-title></periodical><pages>278-287</pages><volume>248</volume><number>1</number><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>135 estimated QALY gains from a single screen at 0.01 to 0.02 per person, producing an ICER in excess of ?106,000 per QALY. The ICER for the annual screening of ever-smokers was at least ?85,000 per QALY gained. Second, a US stage-shift model ADDIN EN.CITE <EndNote><Cite><Author>Pyenson</Author><Year>2012</Year><RecNum>137</RecNum><DisplayText>(143)</DisplayText><record><rec-number>137</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">137</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pyenson, B.S.</author><author>Sander, M.S.</author><author>Jiang, Y.</author><author>Kahn, H.</author><author>Mulshine, J.L.</author></authors></contributors><titles><title>An actuarial analysis shows that offering lung cancer screening as an insurance benefit would save lives at relatively low cost</title><secondary-title>Health Affairs</secondary-title></titles><periodical><full-title>Health Affairs</full-title></periodical><pages>770-779</pages><volume>31</volume><number>4</number><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>143 using ELCAP protocols and outcomes data, Medicare tariffs, and national survival rates by stage produced baseline ICER estimates in the range ?7,900 to ?17,560 (close to the UKLS estimate cited above). The model was re-estimated for annual screening and yielded baseline estimates of ?19,060 or ?31,800, depending on whether the model’s cancer stage predictions derived from the ELCAP- or the NLST-reported stage shift. ADDIN EN.CITE <EndNote><Cite><Author>Villanti</Author><Year>2013</Year><RecNum>138</RecNum><DisplayText>(144)</DisplayText><record><rec-number>138</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">138</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Villanti, A.C.</author><author>Jiang, Y.</author><author>Abrams, D.B.</author><author>Pyenson, B.S.</author></authors></contributors><titles><title>A cost-utility analysis of lung cancer screening and the additional benefits of incorporating smoking cessation interventions</title><secondary-title>PLoS ONE</secondary-title></titles><periodical><full-title>PLoS One</full-title><abbr-1>PloS one</abbr-1></periodical><pages>e71379</pages><volume>8</volume><number>8</number><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>144 Finally, a study in Israel ADDIN EN.CITE <EndNote><Cite><Author>Shmueli</Author><Year>2013</Year><RecNum>124</RecNum><DisplayText>(130)</DisplayText><record><rec-number>124</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">124</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Shmueli, A.</author><author>Fraifeld, S.</author><author>Peretz, T.</author><author>Gutfeld, O.</author><author>Gips, M.</author><author>Sosna, J.</author><author>Shaham, D.</author></authors></contributors><titles><title>Cost-effectiveness of baseline low-dose computed tomography screening for lung cancer: the Israeli experience</title><secondary-title>Value in Health</secondary-title></titles><periodical><full-title>Value in Health</full-title></periodical><pages>922-931</pages><volume>16</volume><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>130 reported QALY gains of around 0.06 per person screened, along with an ICER of only ?1,005 per QALY gained.ICERs calculated from real data tend to be more transparent than those derived from mathematical models. Transparency facilitates comparison, and comparison reveals several likely explanations for variability in cost effectiveness. To begin with, unit costs differ significantly by health care system. Differences in unit cost enable the corresponding ICERs to differ, even when the same quantity of resources is being used to produce the same outcomes. Unit costs are substantially higher in the USA than they are in the UK and, for that matter, in virtually any other country. By way of example, our UKLS model included full investigation and surgical costs at less than ?10,000 per cancer (see Table 30), yet the US models included a cost of around ?19,000 in the month of surgery ADDIN EN.CITE <EndNote><Cite><Author>Cipriano</Author><Year>2011</Year><RecNum>139</RecNum><DisplayText>(145)</DisplayText><record><rec-number>139</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">139</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cipriano, L.E.</author><author>Romanus, D.</author><author>Earle, C.C.</author><author>Neville, B.A.</author><author>Halpern, E.F.</author><author>Gazelle, G.S.</author><author>McMahon, P.M.</author></authors></contributors><titles><title>Lung cancer treatment costs, including patient responsibility, by disease stage and treatment modality, 1992 to 2003</title><secondary-title>Value in Health</secondary-title></titles><periodical><full-title>Value in Health</full-title></periodical><pages>41-52</pages><volume>14</volume><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote>145 or around ?55,000 for diagnosing & treating localised cancer. ADDIN EN.CITE <EndNote><Cite><Author>Villanti</Author><Year>2013</Year><RecNum>138</RecNum><DisplayText>(144)</DisplayText><record><rec-number>138</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">138</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Villanti, A.C.</author><author>Jiang, Y.</author><author>Abrams, D.B.</author><author>Pyenson, B.S.</author></authors></contributors><titles><title>A cost-utility analysis of lung cancer screening and the additional benefits of incorporating smoking cessation interventions</title><secondary-title>PLoS ONE</secondary-title></titles><periodical><full-title>PLoS One</full-title><abbr-1>PloS one</abbr-1></periodical><pages>e71379</pages><volume>8</volume><number>8</number><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>144 Although smaller than US costs, UK unit costs were significantly higher than those reported in the Israel study. For example, Table 30 costs for bronchoscopy and for lobectomy were, respectively, four-times and three-times greater than their counterparts in Israel. Other things remaining equal, interventions appear the least (most) cost effective in countries where the unit costs are highest (lowest).The cost effectiveness of screening is a function of disease prevalence. With a higher prevalence in the target population, more cases will be detected and more health gains will accrue for the same cost of screening. ADDIN EN.CITE <EndNote><Cite><Author>Whynes</Author><Year>2008</Year><RecNum>92</RecNum><DisplayText>(115)</DisplayText><record><rec-number>92</rec-number><foreign-keys><key app="EN" db-id="p22ttrsdm9xz2jezf2kxpdsaz5a2tzx05rxt">92</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Whynes, D.K.</author></authors></contributors><titles><title>Could CT screening for lung cancer ever be cost effective in the United Kingdom ?</title><secondary-title>Cost Effectiveness and Resource Allocation</secondary-title></titles><periodical><full-title>Cost Effectiveness and Resource Allocation</full-title></periodical><pages>5</pages><volume>6</volume><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>115 In screening programmes and models, prevalence impacts in two ways. First, prevalence will be progressively raised by the gradual restriction of targets to individuals with higher and higher risk statuses. All things remaining equal, therefore, any programme involving a more stringent risk criterion for selection will record a greater yield of significant findings per person screened, and a lower ICER, than one with less strict criteria. Beyond the initial screen, costs and outcomes of screening depend on the positive predictive value (PPV) of subject selection, and the different risk models used to identify targets in different studies have been shown to possess different PPVs. ADDIN EN.CITE <EndNote><Cite><Author>D’Amelio</Author><Year>2010</Year><RecNum>140</RecNum><DisplayText>(146)</DisplayText><record><rec-number>140</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">140</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>D’Amelio, A.M.</author><author>Cassidy, A.</author><author>Asomaning, K.</author><author>Raji, O.Y.</author><author>Duffy, S.W.</author><author>Field, J.K.</author><author>Spitz, M.R.</author><author>Christiani, D.</author><author>Etzel, C.J.</author></authors></contributors><titles><title>Comparison of discriminatory power and accuracy of three lung cancer risk models</title><secondary-title>British Journal of Cancer</secondary-title></titles><periodical><full-title>Br J Cancer</full-title><abbr-1>British journal of cancer</abbr-1></periodical><volume>103</volume><dates><year>2010</year></dates><urls></urls></record></Cite></EndNote>146 Furthermore, it has been shown ADDIN EN.CITE <EndNote><Cite><Author>Kovalchik</Author><Year>2013</Year><RecNum>141</RecNum><DisplayText>(147)</DisplayText><record><rec-number>141</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">141</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kovalchik, S.A.</author><author>Tammemagi, M.</author><author>Berg, C.D.</author><author>Caporaso, N.E.</author><author>Riley, T.L.</author><author>Korch, M.</author><author>Silvestri, G.A.</author><author>Chaturvedi, A.K.</author><author>Katki, H.A.</author></authors></contributors><titles><title>Targeting of low-dose CT screening according to the risk of lung cancer death</title><secondary-title>New England Journal of Medicine</secondary-title></titles><periodical><full-title>New England Journal of Medicine</full-title></periodical><pages>245-254</pages><volume>369</volume><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>147 that a more accurate targeting of subjects in the NLST could have reduced considerably the number needed to be screened to prevent a cancer death, suggesting the trial’s protocol was less efficient (and, implicitly, less cost effective) than it might otherwise have been.Second, UKLS comprised a single screen, although other programmes, and their evaluations, have involved multiple screens. Unless disease incidence is especially high, the prevalence of disease in a population falls between the initial screen and any re-screen. A succession of screens will progressively increase total yield, although the increase at the margin would be expected to be lower than the increase in the costs of additional screening. This proposition can be illustrated by a simple simulation. In one of the USA models, ADDIN EN.CITE <EndNote><Cite><Author>Villanti</Author><Year>2013</Year><RecNum>138</RecNum><DisplayText>(144)</DisplayText><record><rec-number>138</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">138</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Villanti, A.C.</author><author>Jiang, Y.</author><author>Abrams, D.B.</author><author>Pyenson, B.S.</author></authors></contributors><titles><title>A cost-utility analysis of lung cancer screening and the additional benefits of incorporating smoking cessation interventions</title><secondary-title>PLoS ONE</secondary-title></titles><periodical><full-title>PLoS One</full-title><abbr-1>PloS one</abbr-1></periodical><pages>e71379</pages><volume>8</volume><number>8</number><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>144 the proportion of positive results on re-screening was judged to be one-third of positives detected at the initial screen. Were we to assume similarly that a re-screen of UKLS subjects using the UKLS protocol would yield one-third of the outcomes detected in the actual screen, cancer management costs would be correspondingly lower, but screening costs would be the same as for the first screen. The ICER for the re-screen would, at ?19,096 per QALY gained, be more than double the ICER for the single screen. Were the yield to be one-quarter of the original outcomes, the ICER would be even higher, at ?24,173.The USA’s long-running National Lung Screening Trial (NLST) has recently published an estimate of $81,000 per QALY as its mean ICER.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CbGFjazwvQXV0aG9yPjxZZWFyPjIwMTQ8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 148 This figure is nearly seven times greater than the corresponding estimate for the UKLS. Fortunately, the transparency of reporting makes cost effectiveness comparisons straightforward, and Table 32 lists the principal similarities and differences between the two trials’ economic calculations. As is evident, all of the factors noted earlier – local unit costs, intensity of resource use, disease prevalence in the target population, multiple screens – contribute to an explanation of the difference in the ICERs.7.6SummaryOwing to the brief duration of the UKLS trial, observations pertinent to economic evaluation were limited to cost-incurring events associated with screening and the initial management of screen-detected cancers. Expected outcomes of the cancers detected were simulated on the basis of life tables and published survival data from other studies.The baseline estimate for the ICER of once-only CT screening, under the UKLS protocol and with conservative assumptions, was ?8,466 per QALY gained (CI 5,516 to 12,634). Health economic analysis suggests that the intervention could be cost-effective. A fully powered trial in the UK would provide these data but may now not be necessary following publication of the NLST and imminent results from NELSON. The key to establishing a cost effective intervention is to design programmes that maximise efficiency, along the lines of methods used in UKLS.The ICER estimated for UKLS is broadly consistent with the ICERs of other recent studies, once allowance is made for differences in efficiency between screening protocols. In this context, screening can be deemed to be more efficient if (i) costs are lower for given outcomes, or (ii) true positive results amongst the target population are more probable, or (iii) the additional outcomes of re-screening outweigh the additional costs incurred. Other things remaining equal, therefore, ICERs will be higher in programmes where (i) unit costs of detection and management are higher, (ii) lower-risk subjects are invited to be screened, (iii) screens are repeated at frequent intervals.Table 30: Unit costs of procedures, 2011-12HRG codeMean unit cost (?)Lower quartile (?)Upper quartile (?)CT scan (single area, no contrast)RA08A846399MDTCMDT_Oth10448118CT scan with contrast (up to 3 areas)RA13Z135103160Guided needle biopsyDZ03B8635301,119BronchoscopyDZ07607402691PET pre-surgeryRA39Z425295525SurgeryDZ027,5026,1788,338Surgery: biopsyDZ03B4,2951,6515,853Palliative careSD01A340159459Radiotherapy?See text3,039Chemotherapy3,883EBUS1,461Table 31: Assumed survival rates and corresponding parameters for survival estimation??MALES????FEMALES???Survival, %ParametersSurvival, %Parameters??1-year5-year?AB1-year5-year?CDSymptomatic presentationStage 1 or later31100.690.935130.650.95Stage 2 or later2580.75128100.721Stage 3 or later2260.780.92580.751Screen-detectionStage 1 or later90700.10.490700.10.4Stage 2 or later75400.250.4775400.250.47?Stage 3 or later5522?0.450.7?5522?0.450.7Table 32: Comparison of cost effectiveness analyses of NLST (USA) and UKLS (UK)NLSTUKLSInference1Yield over 3 screening rounds = 2.0% of persons screened.Yield from a single prevalence screen = 2.1% of persons screened.UKLS had similar yield over fewer screening rounds.2Screening & workup costs per person screened = $1,965.Screening & workup costs per person screened = ?212 ($303 at current exchange rates).Significantly higher detection costs in NLST (more intensive use of more costly resources).3Net treatment costs per person (screen-detected vs. no screening) = $ treatment costs per person (screen-detected vs. no screening) = ?60 = $86.Higher cost of treatment in NLST.4Costs of patient time and travel to appointments included in total cost (evaluation adopts a social perspective, as recommended by the US Panel on Cost-Effectiveness in Health and Medicine).Costs of patient time and travel to appointments NOT included in total costs (evaluation adopts an NHS perspective, as recommended by NICE).Inclusion of patient costs makes NLST screening & management appear to be more expensive (& less cost-effective).5Outcome calculations based on US life table survival estimates.Outcome calculations based on UK life table survival estimates.Similar methods for outcome estimation were employed.6 QALYs gained per person screened = 0.02 overall (0.03 in the 60-69 years age range).QALYs gained per person screened = 0.03.Expected gains per person screened were essentially similar.7Mean ICER = $81,000 overall, but far lower ($52,000 and $32,000) for the two highest-risk quintiles.Mean ICER = ?8,466 ($12,106).Screening lower-risk individuals inflated NLST’s ICER.CHAPTER 8: CONCLUSION8.1Overview of the main findings from the UKLS trialThe Pilot UKLS trial has been successfully completed with 4,055 individuals randomised into a CT screen arm and a non-screen arm, utilising the Wald Single Screen Design.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYWxkd2luPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48

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ADDIN EN.CITE.DATA 52 The specific successes include the demonstration that a population-based lung cancer screening trial can be undertaken in the UK, using a risk based prediction model to select participants. The UKLS protocol-driven radiology, utilising volumetric analysis, and the UKLS care pathways have ensured the highest level of CT imaging assessment and management of participants, as they progressed through the trial. The most significant clinical finding was the 1.7% prevalence of lung cancer at baseline and that 75% were stage 1, considered to have a good five year clinical outcome. Almost half of the subjects with confirmed lung cancer were in the most deprived socioeconomic quintile. The baseline prevalence rate is considerably higher than that reported by the NLST or NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 trials.The harms and benefits were assessed through a detailed analysis of the psychosocial questionnaires and demonstrated that even though participation in the UKLS trial did cause some distress and anxiety in some of the participants, this was not found to be clinically significant. The cost effectiveness of the UKLS trial was modelled on currently available data, and was shown to be beneath the NICE threshold for funding new technologies; the ICER of screen detection was ?8,466 per QALY gained (CI 5,516 to 12,634) and could be considered to offer value for money within an NHS screening programme. To date no cost effectiveness data have been published for any of the international CT screening trials, thus we await the analysis of the pooled European screening trials data.The UKLS web-based database was central to the organisational success of the trial, enabling continuous monitoring of the participants as they moved through the different stages of the process. Linkage of the UKLS database to the Siemens Lung Care volumetric analysis software provided an excellent methodology for following suspicious CT detected nodules through the repeat scans. Indeed linkage of the various questionnaires as well as all the participant letters, radiology and pathology reports within the database provided a model management system for such screening trials.The major limitation of the pilot UKLS is that it is not in a position to provide useful mortality data at this time; however, it is planned to pool the UKLS data with the largest of the European screening trials, the NELSON,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 and others in 2016. At this point, the mortality data will be released for both arms of the UKLS trial, and combined with the European data sets, to provide both mortality and cost effectiveness data for Europe.8.2Implications of UKLS trial for lung cancer screening implementationThe UKLS pilot has provided or will be able to provide new information about the implementation of a UK lung cancer screening programme in the following key areas:i) improving the diagnostic work-up and management of lung nodules, ii) determining the frequency of screening and its duration, iii) appropriately selecting high risk individuals8.2.1Work-up of CT detected nodulesThe UKLS trial incorporated nodule volumetric software as part of its protocol; this provides a more accurate and reliable measurement of lung nodule size and growth than diameter measurements. Despite this, however, a major implication of the findings from the UKLS trial is the need to lessen the burden of diagnostic workup of indeterminate nodules. There were a total of 536 subjects (i.e. Category 3 and 4), with nodules requiring a repeat scan. Of the 64 category 4 nodules, 41 were found to have lung cancer. On examining the number of UKLS participants referred to the MDT clinic, the false positive rate is 3.6% (114-42/1994 =3.6); whilst the Interval Imaging Rate for the Category 3 nodules is 23.2% (472-9/1994). As this work was undertaken within the context of a trial, we erred on the side of ‘over-reporting’ by incorporating category 2 nodules in the care pathway, and this has provided valuable data. However, in a UK National Screening programme, one would consider a higher cut off for the classification of indeterminate nodules, most likely 100mm3. The nodule management algorithms are likely to adopt a similar approach to that of the recently recommended by the BTS for incidental detected nodules, although there may be a slight difference for a screening population. ADDIN EN.CITE <EndNote><Cite><Author>Callister</Author><Year>2015 (in press)</Year><RecNum>4446</RecNum><DisplayText>(149)</DisplayText><record><rec-number>4446</rec-number><foreign-keys><key app="EN" db-id="2s0pww99w9w9tqes2saxwtxierz5tvv05sfp">4446</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Callister, M.C.</author><author>Baldwin, D.R.</author><author>Akram, A.</author><author>et al.</author></authors></contributors><titles><title>British Society Guidelines for the investigation and management of pulmonary nodules. -on behalf ofthe BTS Standards of Care Committe</title><secondary-title>Thorax</secondary-title></titles><periodical><full-title>Thorax</full-title></periodical><dates><year>2015 (in press)</year></dates><urls></urls></record></Cite></EndNote>149 Work-up of screen positives was meticulous and delivered according to strict protocol. However, sensitivity of the screen can only be reliably estimated on follow up of screen negatives for subsequent cancer. This will be the subject of future research in the UKLS cohort.Figure 17 shows an example of a possible pathway for management of nodules detected by low dose CT in lung cancer screening; it is based upon the successful NELSONPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gS2xhdmVyZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z

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ADDIN EN.CITE.DATA 36 and UKLS model with modifications from more recent publications on size threshold and risk of malignancy. \sTo date, the international lung cancer screening trials, including the UKLS, have not stipulated in their protocols the clinical work-up or the treatment interventions of CT-detected nodules that were referred to the MDT. The UKLS pilot was based at thoracic centres of excellence, where all of the clinical specialties were represented; multidisciplinary management of lung cancer is firmly established in the UK. The policies and pathways for managing suspected or proven lung cancer are summarised in the updated NICE ADDIN EN.CITE <EndNote><Cite><Author>NICE</Author><Year>2011</Year><RecNum>142</RecNum><DisplayText>(150)</DisplayText><record><rec-number>142</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">142</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>NICE</author></authors></contributors><titles><title>NICE clinical Guidelines</title><secondary-title>The Diagnosis and Treatment of Lung Cancer (Update)</secondary-title><tertiary-title>National Institute for Health and Clinical Excellence: Guidance</tertiary-title></titles><dates><year>2011</year></dates><pub-location>Cardiff (UK)</pub-location><accession-num>22855970</accession-num><urls><related-urls><url> and BTSPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MaW08L0F1dGhvcj48WWVhcj4yMDEwPC9ZZWFyPjxSZWNO

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ADDIN EN.CITE.DATA 151 guidelines and the most recent BTS guidelines on lung nodule management. ADDIN EN.CITE <EndNote><Cite><Author>Callister</Author><Year>2015 (in press)</Year><RecNum>4446</RecNum><DisplayText>(149)</DisplayText><record><rec-number>4446</rec-number><foreign-keys><key app="EN" db-id="2s0pww99w9w9tqes2saxwtxierz5tvv05sfp">4446</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Callister, M.C.</author><author>Baldwin, D.R.</author><author>Akram, A.</author><author>et al.</author></authors></contributors><titles><title>British Society Guidelines for the investigation and management of pulmonary nodules. -on behalf ofthe BTS Standards of Care Committe</title><secondary-title>Thorax</secondary-title></titles><periodical><full-title>Thorax</full-title></periodical><dates><year>2015 (in press)</year></dates><urls></urls></record></Cite></EndNote>149 Subjects who enter a CT screening programme and have a possible cancer detected would have access to standard management based on the MDT. 8.2.1.1CT reading for lung nodulesIssues around staffing of lung cancer screening, especially radiological reporting, will need to be addressed, especially when volumetric software analysis is incorporated. The UKLS study has convincingly shown that radiographers can also be used as assistant CT readers to aid radiologists, however, it will be necessary to set up the required training and accreditation systems prior to implementation of a national lung cancer screening programme. The UKLS CT data could contribute to a validated dataset for training and testing in the future.8.2.2 Screening age range/durationThe United States Preventive Services Task Force (USPSTF) have recently recommended screening yearly from 55 to 79 years of age based on their micro-simulation modelling, for people with a smoking history of at least 30 pack years who had smoked within 15 years. However, the trials did use this age range, and the trial evidence necessarily pertains to shorter periods of intervention. The approach taken in UKLS was to use an individual risk score to assess eligibility. The results of UKLS suggest that screening might reasonably be offered from ages 60 to 75. As so few people under the age of 60 were at high risk of lung cancer, the recruitment activity below this age range was not productive. Screening at ages 70-75 might be expected to prevent deaths up to age 79. 8.2.3Screen IntervalA far more difficult problem will be deciding on the screen interval. An annual screen was used in NLST but the cost will be higher than a biennial or longer interval. It is possible to calculate the increase in mortality as a consequence of moving from an annual to a biennial screen. This has been modelled in a recent publication from the UKLS group. Various estimates under different potential scenarios suggest that 20-40% more lives might be saved with annual screening rather than biennial.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EdWZmeTwvQXV0aG9yPjxZZWFyPjIwMTQ8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 152 Follow-up of the UKLS population, to assess the time elapsed until the lung cancer incidence and mortality in the intervention and control groups converge will give valuable information in terms of a safe interval. We suggest a strictly applied protocol for screen interval, based on our cost effectiveness modelling given above.8.2.4Selection of high risk individualsThere is clearly an imperative to offer screening specifically to those individuals most likely to benefit from the intervention. Therefore, a robust risk score should be used to select people for screening and this can be done at any point of recruitment, provided a validated score is available. Some scores select on smoking and age at a population level (as used in NLST) ADDIN EN.CITE <EndNote><Cite><Author>National Lung Screening Trial Research Team</Author><Year>2011</Year><RecNum>34</RecNum><DisplayText>(35)</DisplayText><record><rec-number>34</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">34</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>National Lung Screening Trial Research Team, </author><author>Aberle, D.R.</author><author>Adams, A.M.</author><author>Berg, C.D.</author><author>Black, W.C.</author><author>Clapp, J.D.</author><author>Fagerstrom,R.M.</author><author>Gareen, I.F.</author><author>Gatsonis, C.</author><author>Marcus, P.M.</author><author>Sicks. J.D. </author></authors></contributors><titles><title>Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening</title><secondary-title>N Engl J Med</secondary-title></titles><periodical><full-title>N Engl J Med</full-title></periodical><edition>2011/07/01</edition><dates><year>2011</year><pub-dates><date>Jun 29</date></pub-dates></dates><isbn>1533-4406 (Electronic)&#xD;0028-4793 (Linking)</isbn><accession-num>21714641</accession-num><urls><related-urls><url> while others consider a combination of factors (such as previous respiratory disease, previous cancer, family history of lung cancer, exposure to asbestos), in addition to smoking duration and age (as used in the UKLS).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SYWppPC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 153 The utilisation of a risk prediction model to refine the selection process can also be successfully undertaken by an agency such as Radar, who worked very successfully with the UKLS.UKLS data also provide an opportunity to develop a risk prediction model based not only on the patient’s clinical data, but also one that is integrated with imaging characteristics, and in the future with validated biomarkers.8.3Acceptability of screeningIt is important for future policy to identify whether a screening intervention is acceptable, and to whom it is acceptable. There is a clinical need to ensure the acceptability of CT lung screening to those at higher risk, such as long-term smokers, and a public health imperative to ensure we put measures in place to include the ‘hard to reach’ groups which have the highest rates of lung cancer and to understand the barriers to participating in lung cancer screening. The UKLS team have previously reported that participation was more likely in ex-smokers and those with a higher socioeconomic status ADDIN EN.CITE <EndNote><Cite><Author>McRonald</Author><Year>2014</Year><RecNum>95</RecNum><DisplayText>(94)</DisplayText><record><rec-number>95</rec-number><foreign-keys><key app="EN" db-id="0z9sptpv9r9pf9eaw0e5vaag2e0vvwttdz9p">95</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>McRonald, F. E.</author><author>Yadegarfar, G.</author><author>Baldwin, D. R.</author><author>Devaraj, A.</author><author>Brain, K. E.</author><author>Eisen, T.</author><author>Holemans, J. A.</author><author>Ledson, M.</author><author>Screaton, N. J.</author><author>Rintoul, R. C.</author><author>Hands, C. J.</author><author>Lifford, K.</author><author>Whynes, D. K.</author><author>Kerr, K. M.</author><author>Page, R.</author><author>Parmar, M.</author><author>Wald, N.</author><author>Weller, D.</author><author>Williamson, P. R.</author><author>Myles, J.</author><author>Hansell, D. M.</author><author>Duffy, S. W.</author><author>Field, J. K.</author></authors></contributors><auth-address>1Molecular and Clinical Cancer Medicine, University of Liverpool.</auth-address><titles><title>The UK Lung Screen (UKLS): demographic profile of first 88,897 approaches provides recommendations for population screening</title><secondary-title>Cancer Prev Res (Phila)</secondary-title><alt-title>Cancer prevention research</alt-title></titles><periodical><full-title>Cancer Prev Res (Phila)</full-title><abbr-1>Cancer prevention research</abbr-1></periodical><alt-periodical><full-title>Cancer Prev Res (Phila)</full-title><abbr-1>Cancer prevention research</abbr-1></alt-periodical><dates><year>2014</year><pub-dates><date>Jan 17</date></pub-dates></dates><isbn>1940-6215 (Electronic)&#xD;1940-6215 (Linking)</isbn><accession-num>24441672</accession-num><urls><related-urls><url> in the first 88,897 individuals approached to take part in UKLS.8.4Lung cancer screening and integrated smoking cessationThe financial burden of lung cancer is considerable: the estimated cost to the UK economy is ?2.4 billion each year, ?9,071 per patient annually. This is far higher than the cost of any other cancer despite survival rates being among the lowest. More curative treatment and prevention resulting from integrated screening and smoking cessation programmes has the potential to reduce these costs. Thus the implementation of a UK National Lung Cancer screening programme should make use of the ‘teachable moment’ for smoking cessation, which would cascade into major beneficial health effects for all smoking related diseases.Strengths and limitations of the UKLS pilot trialUKLS is a true population-based study, which approached a specific age group via local PCT records. No other lung cancer screening study has adopted this approach. This has yielded important information about uptake from the general population and highlighted the need for a more targeted approach. The UKLS is also the only trial to use a validated lung cancer risk prediction model and shows that this approach can ensure resources are devoted to those most likely to benefit from screening. UKLS has shown that a protocolled management pathway including advanced radiological techniques effectively manages the detected lung cancer with minimal psychological and physical harm to those without cancer. A limitation of the pilot UKLS is that, as a trial, it might be expected to recruit fewer people than a screening programme. It would not be fair to extrapolate from other screening programmes because of differences in the socio-demographic profile but what can be said is that significant further work is the need to address recruitment from the hard to reach community. UKLS was a pilot trial and was not powered to detect a mortality benefit. The planned full trial was not funded but would have addressed this and would have provided cost effectiveness data, based on this. Instead we had to use the (albeit very robust) NLST outcome data to develop a model to estimate cost effectiveness. Although we have been conservative in our model, this is a weakness of the study. UKLS was also unable to provide information on the value of subsequent yearly screens, either in terms of cancer detection rate or uptake. These data have been provided by other studies including the NLST.8.6Prioritised research recommendationsi. Recruitment: investigate methods to engage the hard to reach in future lung cancer screening and smoking cessation programmes.ii. Risk Prediction modelling: access differing LLP risk levels, (include COPD lung function), which should be utilised in a future programme, together with cost effectiveness of screening.iii. CT nodule care pathway: re-assess the cut off for indeterminate nodules, based on volume and volume doubling time – as outlined in the recent NELSON publications. Also investigate the use of CAD in CT screening.iv. Work-up of CT detected nodules: assess whether national methodologies for the work-up of CT screen detected nodules are appropriate within a screening programme. Investigate significant other findings and outcomes. Develop methodologies to incorporate Coronary Artery Score in future lung CT Scan reports.v. Training and accreditation: Radiologists, as well as considering utilising radiographers in a future CT screening programme.vi. Availability of CT scanners for a future CT screening programme: needs to be assessed.vii. Psychosocial assessment of CT screened individuals over a 15 year lifetime for harms and benefits.viii. Integrated smoking cessation programme with future CT screening programmes: There is a synergetic effect by combining both of these modalities.ix. Screen interval: Assess if annual or biennial after two negative scans is appropriate for a future programme.x. Screening age limit: Assess whether 75 years is the appropriate cut off for lung cancer screening.xi. Pooling the UKLS data with the NELSON data: The main UKLS trial was not funded and the time has now passed to undertake a full trial in the UK, however we need to collaborate with the NELSON trialists to pool the UKLS data and examine the combined mortality and cost effectiveness data. This will be the opportunity to follow up on the outcomes of the UKLS control arm. xii. Planning for a future Lung cancer CT screening programme needs to start now, i.e. recruitment methodologies, service provision, screening programme protocol development.8.7Future prospectsIn summary, the UKLS trial data reported in this document have made a very worthwhile contribution to the international datasets on lung cancer screening. Taking the UKLS pilot trial data in consort with the NLST mortality data, it is clear that the intervention can save lives from lung cancer, and may be practicable in the UK health setting. This should be in conjunction with promotion of, and support for, smoking cessation. There are no alternative strategies for significant reductions in lung cancer death rates that can be recommended now or in the next few years.While our results suggest that screening with UKLS eligibility and recruitment policies could be cost-effective, there is some uncertainty around this. There is a need for further research into methods of identification of the appropriate population, and delivery of the service to that population. Recruitment into screening programmes based on risk assessment has to be done in the most effective way, and uptake of screening needs to be monitored. Further research on the inter-screening interval is indicated. Special consideration should also be given to recruiting those in potentially hard to reach sections of society including those with high levels of socio-economic deprivation.Any screening programme will need ongoing monitoring and evaluation, with risks and benefits regularly measured. Such risks and benefits should be conveyed to those offered screening to facilitate informed choice. There is a need to develop both information materials and protocols for regular audit and evaluation of a lung cancer screening programme, so that the National Screening Committee can deliberate on the subject with full knowledge of the implications.3 million offered lung cancer screening~30% uptake and have risk assessment1m respond10% meet risk assessment of 5% over 5 years (UKLS)Annual screen for 10 years 100,000 individuals1,000,000 screening episodes154,000 further investigations over 10 years13857 Screen detected cancers3187 deaths prevented1523 lung cancers over-diagnosedFlow Chart showing outcome model of 1m individuals at high risk, offered annual screening over 10 years, based on the modelling outlined in Duffy et al 2014.Note that the number of further investigations is based on the results of NLST and that with better follow-up protocols this will be reduced. Use of volumetry will reduce the need for further imaging and better predictions of malignancy will reduce the number of minimally invasive and invasive tests3 million offered lung cancer screening~30% uptake and have risk assessment1m respond10% meet risk assessment of 5% over 5 years (UKLS)Annual screen for 10 years 100,000 individuals1,000,000 screening episodes154,000 further investigations over 10 years13857 Screen detected cancers3187 deaths prevented1523 lung cancers over-diagnosedFlow Chart showing outcome model of 1m individuals at high risk, offered annual screening over 10 years, based on the modelling outlined in Duffy et al 2014.Note that the number of further investigations is based on the results of NLST and that with better follow-up protocols this will be reduced. Use of volumetry will reduce the need for further imaging and better predictions of malignancy will reduce the number of minimally invasive and invasive testsFigure 18: Screening Flow ChartAcknowledgementsWe would like to thank the external members of the Trial Steering Committee for their advice and support for the project: Professor Ian Jacobs (University of Manchester), Professor Peter Armstrong (Radiologist, retired), Professor Deborah Ashby (Imperial College, London), Dr Sanjay Popat (Royal Marsden) and Mr David Ardron as our service user representative. Our thanks go also to the Data Monitoring and Ethics Committee comprising Professor Allan Hackshaw (University College, London), Dr Catherine Hill (Institut Gustave Roussy, France) and Dr Robert Smith (American Cancer Society).We are grateful to the participants and GPs who supported the study, giving so generously of their time and sharing their experiences with us. Likewise, the practice managers and administrative staff at all of the collaborating practices and PCTs who provided valuable assistance to us throughout the study.We would like to thank a number of people who helped towards the successful completion of the study:Christopher Hands managed the project for a period of time at inception. Gary Jeffers, Chantelle Murphy and Julie-Ann Bevan, contributed to collecting data for the study and were responsible for sending all scan results letters to participants and their GPs. Dr Victoria Hughes was instrumental in setting up and running the trial within Papworth Hospital. Dr Margarita Perez-Casal gave invaluable support and advice in Liverpool Heart & Chest Hospital. Dr Sadia Anwar, Dr Helen Roberts, Miss Susan Allen, Miss Emily Reason, Miss Clare Darlington, Mrs Susan Maycock (all from Nottingham University Hospitals), the patient volunteers and Mrs Lynn Faulds-Wood gave invaluable help with the production of the UKLS recruitment filmIndividuals at Papworth Hospital who ensured the smooth running of the trial and recruitment of participants - Lucie Garner, Sarah Lewis, Anne Joy, Geri Barrett, David Kirby, Helen Holcombe, SallyAnne Meakins, Leigh Clements, Katie Rintoul Adama Fofana, Angela Reed, Richard Rowlands. Liverpool Heart & Chest Hospital – Diane Jones, Colin Monaghan, Pauline Schofield, Lindsey Hodgson, Nicola Hoy, Peris Widdows, Sarah Feeney, James Chetter, Sheila Whyte, Claire McVey, Frances Johnston, Sharon Steele.Royal Brompton & Harefield NHS Trust – Christine Peacock, RBH Radiology Services Manager - for releasing radiographers for CT reading and the radiographers themselves - Natalie Gartland and Bruce BartonIndividuals at Cardiff University for their help with statistical analysis - Ben Carter (statistical advice for the psychosocial analysis) and Noor Ali (contribution to the analysis of non-participation data). The PACS managers at the local and central sites for their support and advice in the storage and transfer of CT images David McCreavy (LHCH), Tom Wright (PH) and Adam Bell (RBH)]Thank you to Liverpool Cancer Trials Unit for their support and guidance throughout the inception, delivery and reporting of the UKLS trial.Finally, a special mention for Dr Joan Austoker who was heavily involved in the psychosocial elements at the inception of the UKLS pilot trial but who sadly passed away suddenly before the trial started.Contributions of authorsProfessor John K Field (Clinical Professor of Molecular Oncology); Chief Investigator UKLS, developed trial protocol, oversight of the management of trial and analysis; management of staff and funding; preparation and writing final report, editing manuscript.Professor Stephen Duffy (Professor of Screening); Lead UKLS statistician; member of UKLS Trial team; developed trial protocol, preparation of analysis plan; preparation and writing final report, editing manuscript.Professor David Baldwin (Consultant Respiratory Physician and Honorary Professor of Medicine), Respiratory lead for UKLS; member of UKLS Trial team; Chaired the UKLS respiratory review group; assisted in developing trial protocol, external evaluation of clinical data; preparation and writing final report, editing manuscript.Dr Kate Brain (Reader); UKLS Psychosocial lead; member of UKLS Trial team; developing questionnaires and evaluation of data; preparation and contribution to the final report, editing manuscript.Dr Anand Devaraj (Consultant Radiologist); UKLS Radiology second reader and signed off on all trial radiology reports; preparation of data for report preparation and contribution to the final report, editing manuscriptProfessor Tim Eisen (Consultant medical Oncologist); member of UKLS Trial team; provided advice for development of the UKLS trial, editing the final report.Mrs Beverley Green (UKLS Project Manager); oversight of the trial management; supervising staff; reporting to ethics and HTA; preparation of data for and contribution to the final report, editing manuscript.Dr John Holemans (Consultant Radiologist); UKLS radiologist undertaking all CT reporting at the LH&CH; preparation of reports; editing manuscript.Mr Terry Kavanagh (Lung Cancer Patient Representative); member of UKLS Trial team; provided advice on the UKLS trial application, all the patient letters and information sheets. Attended trial meetings, gave patient’s perspective. Dr Keith Kerr (Consultant Pathologist); member of UKLS Trial team; provided advice on the UKLS pathology protocol; editing manuscript.Dr Martin Ledson (Consultant Respiratory Physician); UKLS respiratory physician undertaking the management of all patients referred through LH&CH MDT, reporting at the LH&CH; preparation of reports; editing manuscript.Dr Kate Lifford (Research Fellow); undertook the detailed analysis of the Psychosocial questionnaires, prepared reports.Dr Fiona McRonald (Research Fellow in Molecular Oncology); contributed to analysis of trial recruitment data, collated data with statisticians, took the lead in the preparation of the final report.Dr Arjun Nair (Clinical Research Fellow in Radiology); Undertook the analysis of the CT reporting systems of the radiologists and radiographers involved in the UKLS trial; preparation of reports.Dr Richard Page (Consultant Surgeon); member of UKLS Trial team; provided advice on surgery and management of individuals with CT detected nodules protocol; editing manuscript.Professor Mahesh Parmar (Professor of Medical Statistics); member of the UKLS trial team, provided advice on the development of the UKLS protocol and running the trial; editing manuscript.Dr Robert Rintoul (Consultant Respiratory Physician); was supported, in part, by the Cambridge Biomedical Research Centre and the Cambridge Cancer Centre. UKLS respiratory physician; assisted in setting up the Papworth UKLS recruitment clinics and oversight of the staff; undertaking the management of all patients referred through Papworth Hospital MDT; preparation of reports; editing manuscript. Dr Nicholas Screaton (Consultant Radiologist); UKLS radiologist undertaking all CT reporting at Papworth Hospital; preparation of reports; editing manuscript.Professor Nicholas Wald (Professor of Epidemiology); UKLS advisor on trial design, developed the Wald single-screen design for the UKLS, Member of the UKLS Trial team; provided advice on the development of the UKLS protocol and running the trial; editing manuscript.Professor David Weller, (Director of Centre for Population Health Sciences ); member of UKLS Trial team; provided advice on the UKLS protocol, recruitment; interaction with GP practices, editing manuscript.Professor David Whynes (Professor of Health Economics); UKLS Health Economist; member of the UKLS Trial team; undertook the cost effectiveness modelling for UKLS, preparation of report and editing manuscript.Professor Paula Williamson (Professor of Medical Statistics); member of the UKLS trial team; provided clinical trial support in Liverpool; provided advice on the development of the UKLS protocol and running the trial; editing manuscript.Dr Ghasem Yadegarfar (Research Fellow Statistician); UKLS statistician working within LCTU, undertook the down loading of data from the UKLS database, data cleaning and undertaking analysis on Stata; preparation of statistical reports.Professor David Hansell – (Consultant Radiologist and the recipient of a National Institute of Health Research Senior Investigator Award); Lead UKLS radiologist; member of the UKLS trial Team; chaired the UKLS radiology review group; assisted in developing trial protocol; preparation and writing final report, editing manuscript.Data SharingWe are committed in principle to data sharing with fellow-researchers, and are currently drawing up operating procedures for such. We anticipate that the data will be stored securely In Liverpool and that reasonable requests for data for further research will be accommodated, subject to compliance with regulations, maintaining the integrity of information governance and ensuring no loss of confidentiality on the part of the participants of the study. For requests which require considerable data manipulation and management on our part, this will need to be resourced by those requesting data.PublicationsBaldwin DR, Duffy SW, Wald NJ, Page R, Hansell DM, Field JK. UK Lung Screen (UKLS) nodule management protocol: modelling of a single screen randomised controlled trial of low-dose CT screening for lung cancer. Thorax. 2011 Apr;66(4):308-13. doi: 10.1136/thx.2010.152066. Epub 2011 Feb 11.Field JK, Baldwin D, Brain K, Devaraj A, Eisen T, Duffy SW, Hansell DM, Kerr K, Page R, Parmar M, Weller D, Williamson P, Whynes D; UKLS Team. CT screening for lung cancer in the UK: position statement by UKLS investigators following the NLST report. Thorax. 2011 Aug;66(8):736-7. doi: 10.1136/thoraxjnl-2011-200351. Epub 2011 Jul 1.Nair A, Baldwin DR, Field JK, Hansell DM, Devaraj A. Measurement methods and algorithms for the management of solid nodules. J Thorac Imaging. 2012 Jul;27(4):230-9. doi: 10.1097/RTI.0b013e31824f83e1McRonald FE, Yadegarfar G, Baldwin DR, Devaraj A, Brain KE, Eisen T, Holemans JA, Ledson M, Screaton NJ, Rintoul RC, Hands CJ, Lifford K, Whynes DK, Kerr KM, Page R, Parmar M, Wald N, Weller D, Williamson PR, Myles J, Hansell DM, Duffy SW, Field JK. The UK Lung Screen (UKLS): demographic profile of first 88,897 approaches provides recommendations for population screening. Cancer Prev Res (Phila). 2014 Mar;7(3):362-71. doi: 10.1158/1940-6207.CAPR-13-0206. Epub 2014 Jan 17.Field JK, Hansell DM, Duffy SW, Baldwin DR. CT screening for lung cancer: countdown to implementation. Lancet Oncol. 2013 Dec;14(13):e591-600. doi: 10.1016/S1470-2045(13)70293-6.Duffy SW, Field JK, Allgood PC, Seigneurin A. Translation of research results to simple estimates of the likely effect of a lung cancer screening programme in the UK. Br J Cancer. 2014 Feb 13. Doi 10.1038/bjc.2014.63 Baldwin DR, Hansell DM, Duffy SW, Field JK. Lung cancer screening with low dose computed tomography. BMJ. 2014 Mar 7;348:g1970. doi: 10.1136/bmj.g1970.Field JK. Perspective: The screening imperative. Nature. 2014 Sep 11;513(7517):S7. doi: 10.1038/513S7a.Field JK, Oudkerk M, Pedersen JH, Duffy SW. Prospects for population screening and diagnosis of lung cancer. Lancet. 2013 Aug 24;382(9893):732-41. doi: 10.1016/S0140-6736(13)61614-1.Ali N, Lifford KJ, Carter B, McRonald F, Yadegarfar G, Baldwin DR, Weller D, Hansell DM, Duffy SW, Field JK, Brain K. 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Epub 2013/01/17.Appendix 1:Details of Protocol AmendmentsDetails of Protocol ChangeVersion and DateMinor administrative changes were made to this version of the protocol.? Appendices were also amended to reflect the changes made to other related trial documentation.Version 2: 10MAY2011An increase to the volume of blood collected from participants (from 20 to 24ml) was documented here.Version 3: 20SEP2011A clarification to the exclusion criteria was made here. It was made clear that previous Chest CT scans rather than previous CT scans within the last year preclude participants from joining the trial.Version 4: 16NOV2011The increase to the number of participants approached by UKLS from 82,000 to 250,000 was documented within this amendment.Version 5: 27FEB2012The protocol was amended to reflect nodule management for new nodules identified at the 3 month repeat scan.Version 6: 04APR2012Changes were made in protocol version 7 to detail elements of psychosocial analysis. Changes were also made to the nodule care pathway to clarify detail of how nodule growth is measured, and to allow radiological and clinical decisions to be made in order to facilitate appropriate referrals to the Multi-Disciplinary Team.Version 7: 18MAY2012A change to the frequency of blood collected from participants referred to the MDT was included in this amendment.? In addition, the location where tumour tissue is to be stored was changed. Clarification was made to the frequency of psychosocial questionnaires that are sent to participants.Version 8: 17JUL2012 The requirement for follow up scans to have a second read by the radiologist in the Royal Brompton was removed. It was agreed that a 10% random sample would take place each month and the radiologist in the Royal Brompton would also assist with queries from the radiologists at the sites.Version 9: 28MAR2013 There was a change to the protocol to allow a further psychosocial questionnaire to be sent to participants two weeks after receiving the result of their follow up scan.Version 10: 13JUN2013 We send out psychosocial questionnaires to individuals post-randomisation to the non-screen arm. Individuals in the screen arm receive a psychosocial questionnaire two weeks after they receive each CT scan results letter. We will also be randomly selecting a sub-set of individuals from each arm to receive a psychosocial questionnaire at various time points over the ten year follow up period. The protocol was amended to reflect the fact that we will send all active participants a psychosocial questionnaire at certain time points, the first being before the end of 2013.The protocol was amended to reflect the fact that we will be requesting Hospital Episode Statistics (HES) data to provide follow up data alongside the ONS and Cancer Registry data we currently receive.The protocol was amended to reflect the fact that we will write to GPs to request information on the treatment given to participants referred with significant incidental findings.The protocol now includes clarification on the analysis which will be carried out on participants’ specimens.Version 11: 27SEP2013 This UKLS amendment is based on a retrospective change to the Liverpool Lung Project Risk model, to reflect correctly that the LLPv2 Risk model was utilised in the calculation of risk for all of the UKLS participants (see Appendix 2).Version 12: 21JAN2014Appendix 2:Details of the LLPv2 Risk Model Individuals were selected as eligible to take part in the trial on the basis that their risk of developing lung cancer over the next five years was 5% or greater, based on the Liverpool Lung Project (LLP) risk algorithm. In practice, a lower cut-off point of 4.5% was used, in order to correct for a potential underestimation of risk caused by grouping the start and stop smoking ages into ten-year bands.In addition, risks for the UKLS trial were calculated using LLP version 2 (LLPv2), a modified version of the published LLP model that incorporates other potential respiratory risk factors (bronchitis, emphysema, TB and COPD) in addition to pneumonia; and treats cigar and pipe smoking as conferring an identical risk to cigarette smoking. This LLPv2 model was under development as an alternative method of calculating risk, and the circumstances leading to its use in the trial are described below.A protocol deviation within the UKLS trial arose due to a computing error, whereby the prototype LLPv2 was used for risk calculation instead of the validated basic LLP model. The protocol deviation was confirmed to the UKLS Project Management Team on 17th August 2013. The impact of the computing error was that a proportion of individuals who had been recruited into the UKLS trial did not reach the high risk criterion as defined by the basic LLP risk model (956 subjects; 469 in the non-screen arm and 487 in the screen arm).A comparison of lung cancer prevalence was performed for individuals whose original (published LLP) risk score was >5% and <5% risk over 5 years. This indicated a lung cancer prevalence of 1.65% and 1.64% respectively. It was of note that 93% of the 956 ineligible trial participants had a >3% risk of lung cancer, as defined by the original LLP model; this would be considered sufficiently high risk for inclusion in other lung cancer screening trials.The protocol deviation was reported to both the Trial Steering Committee (TSC) and the Data Monitoring Committee (DMC). The opinion of the Chairs of both committees was that this was an unfortunate event based on a computing error, but that all of the participants should continue to be included in the overall analysis, particularly as those erroneously included had the same prevalence of lung cancer as those recruited according to protocol. On 1st October 2013, the Ethics Committee indicated that they also agreed with this course of action. In addition, the Sponsor and HTA were informed; they too agreed with the action taken.The prevalence of lung cancer in the UKLS screened participants accidentally included due to other respiratory illnesses (e.g. COPD, bronchitis, emphysema, tuberculosis) or smoking of cigars or pipes, was similar to the prevalence in those correctly included (1.64% compared to 1.65% at the time of analysis). This observation was so striking that, if the UKLS were to continue recruitment, the Project Management Team would submit a protocol amendment to include these subjects in future (i.e. if the Main UKLS trial were funded).The reporting of the methodology, risk calculation and selection criteria used in the pilot UKLS trial therefore reflects the actual use of LLPv2. Additional respiratory diseases (ie, COPD, emphysema, bronchitis and TB) were added to the model alongside pneumonia. Pipes and cigars were also added into the model alongside cigarettes. This was not validated in other datasets but confirmed within the UKLS population. This is the case in the current report, and in associated publications.Appendix 3:UKLS Trial Documentation(Please see visit for trial questionnaires) UKLS First Invitation Letter - the original approach to 250,000 individuals in the populationDirector of Public Health PCT DATE Dear (Name) We are writing to ask you if you would be willing to help us in our research efforts in setting up a national screening program to screen for lung cancer. Lung cancer kills more people than any other cancer and is very difficult to treat as it is usually diagnosed in its late stages. We are sorry if we are writing to you at a time when you may already be affected by this disease, however, your name has been chosen at random. The purpose of this study is to determine whether screening will detect lung cancer at an earlier stage, when treatment is more effective and could therefore help prevent deaths from lung cancer. The Department of Health have given us a large grant for this study. The study is called the UK Lung Cancer Screening Trial (UKLS). We need to involve 4,000 men and women, half of whom will be screened and the other half will have no screening. The screened group will have a special x-ray called a CT scan. If you are willing to help us, a computer will select the people and later on decide which group you could be in. The results will decide whether the NHS should start a screening programme for lung cancer, alongside those for cervical and breast cancer. Recruitment clinics are being held at Papworth Hospital near Cambridge and Liverpool Heart and Chest Hospital. You have been randomly selected from individuals aged 50 to 75 years of age from your local Primary Care Trusts. We would be grateful if you would read the enclosed UKLS fact sheet and then complete the ‘UKLS Questionnaire’ and return it in the enclosed envelope. If the computer selects you to participate based on the questionnaire answers we will send you further information on the UKLS trial. If you decide not to participate we would be grateful if you could complete the short questionnaire “Not wishing to participate in the UKLS” and return in the enclosed envelope. We will not contact you again. It will not affect your healthcare in any way if you do not take part. We hope that you will want to take part in the study because it will help us find out if screening can prevent deaths from lung cancer Yours sincerely, Director of Public Health – (PCT) UKLS Contact Details UKLS Patient Information Leaflet – enclosed with first invitation letterUnited Kingdom Lung Cancer Screening Trial (UKLS)This fact sheet gives an overview of the trial. A more detailed information booklet will be sent if you decide to take part. Why do we need a screening test for lung cancer? Lung cancer is the most common cause of death from cancer in the UK. The majority of people unfortunate enough to develop this cancer have few symptoms until it has spread outside the lungs. By this time it is difficult to treat and most cases are incurable. In contrast, treatment is more successful and the outlook is good for the small proportion of people diagnosed before the lung cancer has spread. If we are able to find lung cancer at this early stage through screening when treatment is more effective, then we could prevent people dying from lung cancer. What screening test is being used? We will be using a CT (computed tomography) scan. This is an x-ray of the chest. Am I eligible to take part? In order to be eligible to take part you must: Be 50-75 years of age. Not have had a previous diagnosis of lung cancer. Not currently be taking part in any other screening programme for lung cancer. What does taking part involve? You will need to fill out the enclosed “UKLS Questionnaire” and return to us. A computer will look at your answers and may then select you to take part in the trial. You will be sent a more detailed information booklet about the trial and an appointment to attend your local recruitment centre. At the recruitment centre you will watch a DVD about the trial and then meet a research nurse. You will be asked to register for the trial and the nurse will then take a sample of blood, a mouth swab or wash and a nose brush. You will be given a pot to take home to send back to us with a deep cough sample. A computer will decide whether you are to be in the group to be screened or not to be screened. The groups are chosen at random and are not dependent any information you give us. If you are in the screening group you will be sent an appointment for a CT scan. If you are not in the screening group you will not be required to attend any further appointments. If an abnormality is found by your CT scan, you will be referred for further tests to an NHS specialist. You may be asked to complete a number of health related questionnaires after you join the trial. How long will the study last? The study will take 10 years to complete. If you agree to take part you will be followed up for 10 years through your medical records. How do I take part? Please fill out the enclosed “UKLS Questionnaire” and return to us in the enclosed envelope. Thank you very much for taking the time to read this fact sheet. There will hopefully be a great benefit to future generations, both in prevention as well as early diagnosis and improved treatment of lung cancer.UKLS Patient Information Booklet – sent to participants responding positively to the first invitation letterUKLS Patient Information Booklet – a more detailed information booklet which was sent out with the second questionnaire once an individual had indicated they wanted to participateLung cancer, screening and the UK Lung Cancer Screening (UKLS) trialAn information booklet for people thinking about taking part in the UKLS lung cancer screening trial.Liverpool Heart and Chest Hospital Papworth HospitalContents Page numberInvitation to take part in the UKLS Trial3What is the aim of this booklet?3What is lung cancer?3Who is at risk of developing lung cancer?4What is lung cancer screening?4What is the lung cancer screening trial?5Why am I being invited to take part?5Do I have to take part?5What will happen to me if I take part?6What are lung function tests?7What is the computed tomography (CT) scan? 8What are the possible side effects of CT scans?8When do I get my CT results and what do they mean?9What happens if my CT scan finds something?9How reliable is lung cancer screening?10Are there any risks if I take part?10What are the possible benefits of taking part?10Where do I need to go?10What happens if new information becomes available during the trial?11What happens when the trial stops?11What will happen to the results of the trial?11Will my samples be used for any further research?11Will my taking part in the trial be kept confidential?12What happens if something goes wrong?12Who is co-ordinating and funding the trial?12Who has reviewed the trial?12I am a smoker but would like to stop smoking. Can I still take part?12Where can I get more information?13What should I do now?13Invitation to take part in the UKLS TrialWe would like to invite you to take part in a large national research project which we hope will benefit people at risk of developing lung cancer. Before you decide whether or not to take part, it is important for you to understand why the research is happening and what it will involve. Please take time to read this information booklet carefully and discuss it with your family and friends if you wish. What is the aim of this booklet?This booklet tells you about lung cancer, lung cancer screening and how the trial will work. It aims to give you information that will help you choose whether or not you wish to take part in the UK Lung Cancer Screening (UKLS) trial. What is lung cancer?Cancer develops when cells become abnormal and grow out of control. Over time they form a clump known as a tumour. Lung cancer develops in the tubes that carry air in and out of the lungs (your airways) or within the lung. It can then spread outside the lung to other parts of the body.Lung cancer often develops slowly. It is thought that cells first become abnormal at least five years before the cancer can be detected. The reasons for this delay include: most tumours grow slowly the lungs are large the lungs do not feel pain some of the symptoms are similar to those of other lung problems such as bronchitis or chronic obstructive pulmonary disease (COPD). By the time lung cancer is diagnosed, it has often spread outside the lung. If this happens, the cancer can only rarely be cured. The UKLS aims to discover whether finding lung cancer at a very early stage means that it can be treated successfully. Who is at risk of developing lung cancer?Anybody can develop lung cancer, but people who smoke (or used to smoke) are at the greatest risk. The risk increases with the total number of cigarettes you smoke. If you stop smoking, the risk gets less over time. Passive smoking (breathing in someone else’s tobacco smoke) over a long period of time may also cause lung cancer. Non smokers may develop a particular type of lung cancer (called adenocarcinoma). This can happen in a part of the lung where damage has happened for a number of other reasons (such as breathing in asbestos). What is lung cancer screening?Lung cancer screening uses an advanced x-ray technique, called computed tomography (CT) scanning, in order to find lung cancer before symptoms develop so that early treatment can remove the cancer and deaths from lung cancer may be prevented. More people die from lung cancer in the UK every year than from any other form of cancer. About 40,000 people develop lung cancer each year in the UK and many of these are smokers. However, non-smokers and ex-smokers can also develop lung cancer. Finding lung cancer by screening instead of symptoms may mean that treatment is more effective. Lung cancer screening aims to find changes in the lung at the time of the scan but it cannot prevent you from getting lung cancer in the future. Only one randomised controlled screening trial (NLST) so far has shown that screening by CT scanning is effective. The NLST trial has been recently published and was conducted in the USA. The results showed that the mortality from lung cancer was reduced by 20%. Thus it is likely that in the same population, CT screening is effective. However, populations differ and there are other questions that remain unanswered, regarding the integration of CT screening into a national programme and which patients benefit most. The unanswered questions led a group of experts from many countries to conclude that further trials (including UKLS) were needed to answer these important questions and to guide the design of future screening programmes.What is the lung cancer screening trial?The United Kingdom Lung Cancer Screening (UKLS) trial is a study of lung cancer screening in the UK. We aim to recruit 4,000 people in the first stage of the trial. If the results of our trial show that CT scanning of the lungs in healthy people is worthwhile then, in the future, CT scanning may be used throughout the country for lung cancer screening. This would be similar to the breast, cervical and bowel cancer screening programmes used today. Why am I being invited to take part?You have provided details about your lifestyle and based on this information we would like to invite you to take part in the UKLS trial. We want to recruit people aged 50 to 75 years old. We have chosen this age group because we believe that if lung cancer screening is shown to be worthwhile, these are the people most likely to benefit. Do I have to take part?No, it is up to you to decide whether or not to take part. If you decide to take part you will be given this information booklet to keep and asked to sign a consent form. You are still free to change your mind or withdraw from the trial at any time and without giving a reason. If you decide not to take part, we will not contact you again and the care you receive from your general practitioner (GP) will not be affected. We would, however, be grateful if you could fill out the attached questionnaire called ‘UKLS non-participant questionnaire’ and return it to us. This information is very important and will help us to plan future screening programmes.What will happen to me if I take part?You have already been asked some basic questions about smoking, your lifestyle, occupation and known health problems. The next stage is an invitation to a recruitment clinic.At the UKLS recruitment clinic, you will be met by the receptionist and invited to watch a DVD which will give you further information about the trial. You will then be met by a UKLS research nurse who will answer any questions you may have and will then ask you to sign a consent form. At this point you will be enrolled into the trial. You will then be asked some more detailed questions about your lifestyle and family history of disease. You will be asked to give your answers on a computer by touching the screen, which will greatly assist us in developing a better way of identifying individuals for lung cancer screening programmes. You will also be asked to blow into a tube a few times (lung function tests) to look at the health of your lungs. A member of the UKLS team will take a sample of blood, a mouth swab or wash and a nose brush. The nasal sampling has a very small risk of causing a nose bleed. You will be given a pot to take home so that you can return a deep cough sample, which will be explained to you at the recruitment visit. Finally, if you are a current smoker, you will be provided with information to help you stop smoking. Your visit to the recruitment clinic should take about an hour. Your GP will be informed that you are a participant in the UKLS trial.To find out whether CT scanning of the lungs will be beneficial, we need to divide those who join our trial into two groups. Half of the people joining the trial will be offered a CT scan of their lungs and the other half will not. Both groups are equally important because we are going to observe what happens in each group. Then, at the end of the trial, we shall compare what happened to the group who were scanned to what happened to the group who weren’t. To run the trial properly, people must be put into a group at random (using a computer), so neither you nor your doctor can choose which group you are put into. You will find out which group you are in about four weeks after you are enrolled.In this trial we would also like to look at the effect of screening on the well-being of those who take part, so you will be asked from time to time to take part in surveys about your experience of the UKLS. Regardless of whether you are in the screening or non-screening group, we will check on your health through your medical records over the next 10 years. If for any reason during the trial, you lose the capacity to consent, we will retain all the previously collected data and specimens, however, we will not contact you again with any questionnaires or follow-up appointments.What are lung function tests?Lung function tests (also known as pulmonary function tests) are used to look for the cause of breathing problems (like shortness of breath) and are also used to see how well treatments for breathing problems, such as asthma medicines, are working. They can be used to check for conditions such as asthma, lung tissue scarring, sarcoidosis, and COPD (chronic obstructive pulmonary disease). The tests may also be used to check whether a condition is getting worse. Lung function tests measure:How much air you can take into your lungs. This amount is compared to that of other people your own age, height, and sex;How much air you blow out of your lungs and how fast you do it;How well your lungs deliver oxygen to your blood; andHow strong your breathing muscles are. What is the computed tomography (CT) scan?If you are part of the screening group, you will have a CT scan of your lungs. This scan of the chest uses x-rays and a computer to produce pictures of the lungs (it’s also known as CAT scanning). During the CT scan you lie down on the scanner and hold your breath for a few seconds while pictures of your lungs are taken. Your appointment should take about 15-30 minutes. All of the CT scan pictures are stored in a computer and read by a radiologist. You will be told about the results of your scan by a clinical member of the UKLS team within four weeks of the scan. If no abnormalities are found, you will not need any further tests but we will contact you to fill out questionnaires so that we can find out about your future health and how you felt about being screened. If any important abnormalities are found you would be given an appointment to see a chest specialist at your local NHS hospital. The specialist may advise follow-up with another CT scan or other tests. If the lung specialist thinks you might have lung cancer, they will discuss treatment options with you. Your GP will be kept informed of your participation in the UKLS trial and of all scan results and any treatment recommendations, and will be able to discuss them with you. What are the possible side effects of CT scans?Because CT scanning uses x-rays to obtain pictures, you will be exposed to some radiation which you would otherwise not receive. However, the dose of x-rays used for the screening CT scan is less than you normally receive in a year from the natural surrounding environment and the risk of it causing harm is therefore very low. If an abnormality is detected, you may need follow-up CT scans and if lung cancer is strongly suspected, further tests will be required. The radiation dose for follow-up CT scans would be higher than for the first but the risk is still low and the benefits of detecting lung cancer at an early stage must be considered. When do I get my CT results and what do they mean?You should receive a results letter from the UKLS trials team within four weeks of having your CT scan. There are three types of results that you could receive; A normal result means no tumour or nodule was found. We expect that most people will receive a normal result. A normal result does not guarantee that you do not have or will never develop lung cancer in the future, so staying aware of any symptoms of lung cancer in the future is very important. An unclear result means that your screening CT scan was not quite normal and further tests are needed. Getting an unclear result may not mean you have cancer, just that you will most likely need a follow-up CT scan. If you receive an unclear result you will be invited back to the hospital to have another CT scan. Most people will then receive a normal result. An abnormal result means that the CT scan may have found signs of a cancerous nodule in your lungs. It is not a diagnosis of cancer, but it does mean that you will be offered a follow-up CT scan and other tests. Sometimes scans show other conditions that are not lung cancer but do require medical attention. If this is the case, your GP will be informed and contact you to ensure the abnormality is dealt with appropriately.What happens if my CT scan finds something?You will be cared for through the NHS and given an appointment with a lung cancer specialist doctor to discuss having a more detailed examination of your lungs. This will involve some further tests and the specialist team will explain what is required. If lung cancer is found, a doctor will discuss treatment options with you and your wishes will be taken into account. If you have to undergo any investigations or treatment procedures, we will collect any left-over tissue specimens for future research.How reliable is lung cancer screening?In some cases lung cancer could be present but may not be found by CT scanning. Cancers in the main airways in the middle of the chest may be difficult to identify on CT and not all small cancers in the lung are recognised, even by the most skilled radiologists. Are there any risks if I take part?Sometimes non-cancerous abnormalities are found by screening which you would otherwise have never known about. If you need an operation to remove a lung abnormality or need to have a lung biopsy any possible risks will be fully explained to you by an NHS specialist before the procedure is undertaken.What are the possible benefits of taking part?As we do not know whether early diagnosis of lung cancer with CT scanning will be beneficial, it is not possible to tell you whether you are likely to benefit from entering the trial or not. Even If the trial is of no benefit to you personally because you have normal lungs it may benefit others taking part in the UKLS trial, as well as people in the future. Where do I need to go?There are two centres taking part in the trial:The Liverpool Heart & Chest Hospital NHS Foundation Trust, Thomas Drive, Liverpool, L14 3PE;Papworth Hospital NHS Foundation Trust, Papworth Everard, Cambridge, CB23 3RE.Both centres have dedicated rooms for the trial. When you arrive, you will be directed to a waiting room and met by the trial receptionist. If you attend the Papworth clinic, you might be offered screening in our on-site mobile screening unit.If you provide evidence that you receive benefits, some support may be available to you for reasonable travel expenses.What happens if new information becomes available during the trial?Sometimes during the course of a research study a new technique or new information becomes available which is relevant to the study. This is unlikely to happen in this trial because the type of CT scanner being used will be up-to-date and of high quality. However, should such a situation arise, the research nurse will tell you about it and discuss with you whether you wish to continue in the study or whether it would be in your best interests to withdraw. What happens when the trial stops?Your records will be under the care of the trial centre for 20 years and your GP will continue your normal care during and after the trial. What will happen to the results of the trial?The main results of this trial are not likely to be available for several years but early results relating to the number of lung cancers found by screening should be known after the first two years. Once carefully analysed, the results will be published in medical journals. If you would like a summary of the results as they become available please inform the research nurse at your trial centre.Will my samples be used for any further research?If you agree to take part in this trial you will be asked to give a blood sample, a sample of spit, a mouth swab or wash and a nose brush. These samples will be stored at the University of Liverpool Biobank and will be used in future research projects with ethical approval. This may allow researchers to identify biological and genetic factors that affect the risk of future lung disease. This may be useful in developing future screening tests or in understanding more about the causes of lung cancer or other lung diseases. The UKLS research team will be responsible for keeping the samples safely and making sure that any information remains confidential. Results of any studies undertaken on your sample will not be fed back to you personally or placed in your medical records, as the tests are for research only. The research results will not have any implications for you personally. All future use of the samples will have to be approved by an Ethics Committee. Will my taking part in the trial be kept confidential?If you consent to take part in this trial, your medical records and CT scan results may be inspected by authorised representatives within the trial and by the Department of Health regulatory authorities. Your name will not be disclosed outside the trial. You will not be identified in any publication or report of the trial. What happens if something goes wrong?Your legal rights are not affected by giving your consent to take part in this trial. Who is co-ordinating and funding the trial?The trial will be co-ordinated by the Liverpool Cancer Trials Unit (LCTU). It is funded by the Department of Health - Health Technology Agency. Who has reviewed the trial?The trial has been reviewed by the Department of Health – Health Technology Agency, the Multi-Centre Research Ethics Committee (MREC) and by the Local Research Ethics Committees of the two centres involved. I am a smoker but would like to stop smoking. Can I still take part?Yes and we would encourage you to stop smoking. Even if you stop smoking at any point in the future you are still able to take part in the trial. Just let us know the date you stopped smoking when you send back your next follow-up questionnaire. The UKLS team will provide you with NHS stop smoking materials or you can contact the NHS on 0800 022 4 332 or go to can I get more information?Please feel free to telephone the research nurse on 0800 195 8557 in the UKLS Project management office if you would like more information or have questions you would like to ask.More information about the trial can be found on the UKLS website What should I do now?We hope that you are encouraged to join the trial. If you would like to take part, please return the enclosed ‘UKLS participant questionnaire’. If however you decide not to take part, please fill in the ‘UKLS non-participant questionnaire’.Thank you for reading this UKLS information booklet and we do hope you will want to participate in this very important Lung Cancer Screening Trial, which may prevent deaths from lung cancer.Appendix 4:Trial randomisation method outlined in Section 2.4.1The same randomisation process had previously been used in the NELSON Lung cancer screening trial; it had therefore been tested and proven fit for purpose, and capable of achieving balance between the screening and control arms.At the beginning of the study, the maximum number of subjects was decided (i.e. 250,000 subjects for UKLS). More than 250,000 random personal ID (PID) codes were then generated, each one consisting of 8 characters (letters and/or digits), and a unique code was allocated to each subject in the UKLS database (positive and negative responders from the first stage). Individuals who had attended the recruitment clinics, and provided written informed consent, were flagged for randomisation. The PID codes corresponding to these people were used in the randomisation process.As an example, consider the randomisation of 11 people who had signed consent forms:Simply by sorting the PID codes corresponding to these people, there is already a random division – i.e. the first goes into the screening arm, the second into the control arm, third into the screening arm... etc. It is therefore pure chance which participants are allocated to which group.To eliminate the possibility of staff influencing the process by knowledge of the PID codes, the randomisation program draws a number between 1 and 7, and rotates the PID code the same number of positions. The resulting shifted PID codes are sorted alphanumerically, thus the splitting of the group into screening and control arms is now performed on this new order. Addition or removal of one person in a group of PID codes to be randomised will impact upon the order of codes and therefore trial arm allocation. This adds a further random element to the process.Example:Original PID codeOriginal orderRandom number shift of PID by 3 positions (original order rank)Random number shift of PID by 6 positions (original order rank)ABDDJ3V11ADXP1DA0? (3)?? 0WHG0ASC (6)CA0AFCKT2AFCKTCA0? (2)3FTGAMBK (8)DA0ADXP13AMS6HVYG (10)6HVYGAMS(10)GA0AW3MZ4ASC0WHG0 (6)KTCA0AFC (2)GA1FDNSA5AW3MZGA0 (4)MQZQ4JGA (11)HG0ASC0W6DJ3V1ABD (1)MXTU8PGA (9)KA1FBXQM7FBXQMKA1 (7)MZGA0AW3 (4)TGAMBK3F8FDNSAGA1 (5)P1DA0ADX (3)TU8PGAMX9JGAMQZQ4 (11)QMKA1FBX (7)VYGAMS6H10MBK3FTGA (8)SAGA1FDN (5)ZQ4JGAMQ11PGAMXTU8 (9)V1ABDDJ3 (1)Appendix 5:Data tables showing demographics of response and recruitmentThis appendix relates to the trial recruitment procedure described in Chapter 3. Sex, age, socioeconomic status, region (North vs. South) and smoking status were analysed with respect to (a) response to the initial invitation letter, (b) risk status, and (c) participation of high risk people in the UKLS screening trial. The numbers involved are tabulated in this Appendix; the data are arranged in sets of tables grouped according to which socio-demographic factor is being considered, as per the layout in Chapter 3. Further tables are included where a more detailed analysis took place, or when two socio-demographic factors were considered together. For the overall figures (on this page), the sections of the table showing the pathway to the RCT are shaded blue. For further clarification as to how the different analysis categories were defined, see the Consort diagram (Figure 3.1 in Chapter 3).(i) OVERALL FIGURES(a) Response to initial invitation letter?Positive ResponseNegative ResponseNo responseTOTALTOTAL75,958 (30.7%)22,788 (9.2%)148,608 (60.1%)247,354 (100.0%)(b) Risk status?High RiskLow RiskUnknown RiskTOTALTOTAL8,729 (11.5%)66,377 (87.4%)852 (1.1%)75,958 (100.0%)(c) Participation of high risk people in the screening trial?ConsentedOpted OutNot EligibleTOTALTOTAL4,061 (46.5%)3,377 (38.7%)1,291 (14.8%)8,729 (100.0%)(ii) SEXGENDERPositive ResponseNegative ResponseNo responseTOTALMale37,525 (30.3%)9,557 (7.7%)76,666 (62.0%)123,748 (100.0%)Female38,433 (31.1%)13,231 (10.7%)71,942 (58.2%)123,606 (100.0%)TOTAL75,95822,788148,608247,354GENDERHigh RiskLow RiskUnknown RiskTOTALMale6,141 (16.4%)30,794 (82.1%)590 (1.6%)37,525 (100.0%)Female2,588 (6.7%)35,583 (92.6%)262 (0.7%)38,433 (100.0%)TOTAL8,72966,37785275,958GENDERConsentedOpted OutNot EligibleTOTALMale3,041 (49.5%)2,198 (35.8%)902 (14.7%)6,141 (100.0%)Female1,020 (39.4%)1,179 (45.5%)389 (15.0%)2,588 (100.0%)TOTAL4,0613,3771,2918,729(iii) AGEAGE GROUPPositive ResponseNegative ResponseNo responseTOTALAge: 50-5516,273 (26.6%)3,502 (5.7%)41,393 (67.7%)61,168 (100.0%)Age: 56-6012,892 (30.3%)3,177 (7.5%)26,476 (62.2%)42,545 (100.0%)Age: 61-6521,226 (35.0%)5,573 (9.2%)33,845 (55.8%)60,644 (100.0%)Age: 66-7017,302 (32.6%)5,817 (11.0%)29,936 (56.4%)53,055 (100.0%)Age: 71-758,265 (27.6%)4,719 (15.8%)16,958 (56.6%)29,942 (100.0%)TOTAL75,95822,788148,608247,354AGE GROUPHigh RiskLow RiskUnknown RiskTOTALAge: 50-5582 (0.5%)16,170 (99.4%)21 (0.1%)16,273 (100.0%)Age: 56-60507 (3.9%)12,331 (95.6%)54 (0.4%)12,892 (100.0%)Age: 61-652,726 (12.8%)18,255 (86.0%)245 (1.2%)21,226 (100.0%)Age: 66-703,368 (19.5%)13,617 (78.7%)317 (1.8%)17,302 (100.0%)Age: 71-752,046 (24.8%)6,004 (72.6%)215 (2.6%)8,265 (100.0%)TOTAL8,72966,37785275,958AGE GROUPConsentedOpted OutNot EligibleTOTALAge: 50-5529 (35.4%)43 (52.4%)10 (12.2%)82 (100.0%)Age: 56-60205 (40.4%)196 (38.7%)106 (20.9%)507 (100.0%)Age: 61-651,298 (47.6%)1,001 (36.7%)427 (15.7%)2,726 (100.0%)Age: 66-701,618 (48.0%)1,277 (37.9%)473 (14.0%)3,368 (100.0%)Age: 71-75911 (44.5%)860 (42.0%)275 (13.4%)2,046 (100.0%)TOTAL4,0613,3771,2918,729Risk Status by Individual year of ageAgeHigh RiskLow RiskUnknown RiskTotal% at high risk5012,04102,0420.05101,78001,7800.05211,91601,9170.15312,36822,3710.054283,98144,0130.755514,084154,1501.256533,50333,5591.557522,30182,3612.258631,97752,0453.1591382,09792,2446.1602012,453292,6837.5612853,001283,3148.6623433,273293,6459.4634213,704494,17410.1647734,360585,19114.9659043,917814,90218.4666583,144613,86317.0677553,229674,05118.6687272,808623,59720.2696542,468673,18920.5705741,968602,60222.1716671,937902,69424.8724461,417371,90023.5733771,062311,47025.674354943301,32726.775*2026452787423.1*The 75 year age group contains 8 individuals who were age 76 at the time of initial contact by the Data Management Company, as they had had a birthday shortly after details of all 50-75 year olds were supplied to the DMC by the PCTs(iv) SOCIOECONOMIC STATUSIMD; allPositive ResponseNegative ResponseNo responseTOTALQ1 most deprived13,893 (21.7%)4,265 (6.7%)45,882 (71.6%)64,040 (100.0%)Q29,813 (27.6%)3,300 (9.3%)22,453 (63.1%)35,566 (100.0%)Q313,337 (30.5%)4,375 (10.0%)26,060 (59.5%)43,772 (100.0%)Q416,707 (34.8%)4,933 (10.3%)26,407 (55.0%)48,047 (100.0%)Q5 least deprived22,198 (39.7%)5,913 (10.6%)27,792 (49.7%)55,903 (100.0%)Missing10 (0.0%)2 (0.0%)14 (0.0%)26 (0.0%)TOTAL75,95822,788148,608247,354IMD; allHigh RiskLow RiskUnknown RiskTOTALQ1 most deprived2,530 (18.2%)11,102 (79.9%)261 (1.9%)13,893 (100.0%)Q21,259 (12.8%)8,425 (85.9%)129 (1.3%)9,813 (100.0%)Q31,558 (11.7%)11,625 (87.2%)154 (1.2%)13,337 (100.0%)Q41,541 (9.2%)15,033 (90.0%)133 (0.8%)16,707 (100.0%)Q5 least deprived1,840 (8.3%)20,183 (90.9%)175 (0.8%)22,198 (100.0%)Missing1 (0.0%)9 (0.0%) (0.0%)10 (0.0%)TOTAL8,72966,37785275,958IMD; allConsentedOpted OutNot EligibleTOTALQ1 most deprived1,090 (43.1%)1,110 (43.9%)330 (13.0%)2,530 (100.0%)Q2487 (38.7%)533 (42.3%)239 (19.0%)1,259 (100.0%)Q3723 (46.4%)589 (37.8%)246 (15.8%)1,558 (100.0%)Q4732 (47.5%)537 (34.8%)272 (17.7%)1,541 (100.0%)Q5 least deprived1,029 (55.9%)607 (33.0%)204 (11.1%)1,840 (100.0%)Missing (0.0%)1 (0.0%) (0.0%)1 (0.0%)TOTAL4,0613,3771,2918,729(v) REGIONREGIONPositive ResponseNegative ResponseNo responseTOTALNorth32,609 (26.4%)9,873 (8.0%)81,049 (65.6%)123,531 (100.0%)South43,349 (35.0%)12,915 (10.4%)67,559 (54.6%)123,823 (100.0%)TOTAL75,95822,788148,608247,354REGIONHigh RiskLow RiskUnknown RiskTOTALNorth4,496 (13.8%)27,674 (84.9%)439 (1.3%)32,609 (100.0%)South4,233 (9.8%)38,703 (89.3%)413 (1.0%)43,349 (100.0%)TOTAL8,72966,37785275,958REGIONConsentedOpted OutNot EligibleTOTALNorth2,050 (45.6%)1,785 (39.7%)661 (14.7%)4,496 (100.0%)South2,011 (47.5%)1,592 (37.6%)630 (14.9%)4,233 (100.0%)TOTAL4,0613,3771,2918,729(vi) REGION AND IMD: NORTHIMD; NorthPositive ResponseNegative ResponseNo responseTOTALQ1 most deprived11,542 (21.2%)3,403 (6.3%)39,448 (72.5%)54,393 (100.0%)Q25,178 (27.1%)1,602 (8.4%)12,338 (64.5%)19,118 (100.0%)Q36,380 (29.1%)1,997 (9.1%)13,567 (61.8%)21,944 (100.0%)Q46,095 (33.3%)1,761 (9.6%)10,467 (57.1%)18,323 (100.0%)Q5 least deprived3,413 (35.0%)1,108 (11.4%)5,226 (53.6%)9,747 (100.0%)Missing1 (0.0%)2 (0.0%)3 (0.0%)6 (0.0%)TOTAL32,6099,87381,049123,531IMD; NorthHigh RiskLow RiskUnknown RiskTOTALQ1 most deprived2,152 (18.6%)9,169 (79.4%)221 (1.9%)11,542 (100.0%)Q2702 (13.6%)4,412 (85.2%)64 (1.2%)5,178 (100.0%)Q3762 (11.9%)5,538 (86.8%)80 (1.3%)6,380 (100.0%)Q4576 (9.5%)5,472 (89.8%)47 (0.8%)6,095 (100.0%)Q5 least deprived303 (8.9%)3,083 (90.3%)27 (0.8%)3,413 (100.0%)Missing1 (0.0%) (0.0%) (0.0%)1 (0.0%)TOTAL4,49627,67443932,609IMD, NorthConsentedOpted OutNot EligibleTOTALQ1 most deprived1,022 (47.5%)902 (41.9%)228 (10.6%)2,152 (100.0%)Q2306 (43.6%)277 (39.5%)119 (17.0%)702 (100.0%)Q3363 (47.6%)283 (37.1%)116 (15.2%)762 (100.0%)Q4252 (43.8%)200 (34.7%)124 (21.5%)576 (100.0%)Q5 least deprived107 (35.3%)122 (40.3%)74 (24.4%)303 (100.0%)Missing (0.0%)1 (0.0%) (0.0%)1 (0.0%)TOTAL2,0501,7856614,496(vii) REGION AND IMD: SOUTHIMD; SouthPositive ResponseNegative ResponseNo responseTOTALQ1 most deprived2,351 (24.4%)862 (8.9%)6,434 (66.7%)9,647 (100.0%)Q24,635 (28.2%)1,698 (10.3%)10,115 (61.5%)16,448 (100.0%)Q36,957 (31.9%)2,378 (10.9%)12,493 (57.2%)21,828 (100.0%)Q410,612 (35.7%)3,172 (10.7%)15,940 (53.6%)29,724 (100.0%)Q5 least deprived18,785 (40.7%)4,805 (10.4%)22,566 (48.9%)46,156 (100.0%)Missing9 (0.0%) (0.0%)11 (0.0%)20 (0.0%)TOTAL43,34912,91567,559123,823IMD; SouthHigh RiskLow RiskUnknown RiskTOTALQ1 most deprived378 (16.1%)1,933 (82.2%)40 (1.7%)2,351 (100.0%)Q2557 (12.0%)4,013 (86.6%)65 (1.4%)4,635 (100.0%)Q3796 (11.4%)6,087 (87.5%)74 (1.1%)6,957 (100.0%)Q4965 (9.1%)9,561 (90.1%)86 (0.8%)10,612 (100.0%)Q5 least deprived1,537 (8.2%)17,100 (91.0%)148 (0.8%)18,785 (100.0%)Missing (0.0%)9 (0.0%) (0.0%)9 (0.0%)TOTAL4,23338,70341343,349IMD, SouthConsentedOpted OutNot EligibleTOTALQ1 most deprived68 (18.0%)208 (55.0%)102 (27.0%)378 (100.0%)Q2181 (32.5%)256 (46.0%)120 (21.5%)557 (100.0%)Q3360 (45.2%)306 (38.4%)130 (16.3%)796 (100.0%)Q4480 (49.7%)337 (34.9%)148 (15.3%)965 (100.0%)Q5 least deprived922 (60.0%)485 (31.6%)130 (8.5%)1,537 (100.0%)Missing (0.0%) (0.0%) (0.0%) (0.0%)TOTAL2,0111,5926304,233(viii) SMOKING STATUSSMOKINGPositive ResponseNegative ResponseTOTALCurrent smoker11,130 (14.7%)2,078 (9.1%)13,208Ex-smoker29,855 (39.3%)8,260 (36.2%)38,115Never smoker32,949 (43.4%)11,686 (51.3%)44,635Missing2,024 (2.7%)764 (3.4%)2,788TOTAL75,958 (100.0%)22,788 (100.0%)98,746SMOKINGHigh RiskLow RiskUnknown RiskTOTALCurrent smoker3,724 (33.5%)7,080 (63.6%)326 (2.9%)11,130 (100.0%)Ex-smoker4,995 (16.7%)24,335 (81.5%)525 (1.8%)29,855 (100.0%)Never smoker10 (0.0%)32,938 (100.0%)1 (0.0%)32,949 (100.0%)Missing (0.0%)2,024 (100.0%) (0.0%)2,024 (100.0%)TOTAL8,72966,37785275,958SMOKINGConsentedOpted OutNot EligibleTOTALCurrent smoker1,568 (42.1%)1,620 (43.5%)536 (14.4%)3,724 (100.0%)Ex-smoker2,491 (49.9%)1,753 (35.1%)751 (15.0%)4,995 (100.0%)Never smoker2 (20.0%)4 (40.0%)4 (40.0%)10 (100.0%)TOTAL4,0613,3771,2918,729(ix) SMOKING, IMD AND RESPONSE STATUSPositive RespondersIMD vs smoking; positive respondersCurrent SmokerEx-smokerNever smokerMissingTOTALQ1 most deprived3,832 (27.6%)5,362 (38.6%)4,142 (29.8%)557 (4.0%)13,893 (100.0%)Q21,659 (16.9%)3,962 (40.4%)3,890 (39.6%)302 (3.1%)9,813 (100.0%)Q31,796 (13.5%)5,410 (40.6%)5,766 (43.2%)365 (2.7%)13,337 (100.0%)Q41,818 (10.9%)6,539 (39.1%)7,960 (47.6%)390 (2.3%)16,707 (100.0%)Q5 least deprived2,024 (9.1%)8,578 (38.6%)11,186 (50.4%)410 (1.8%)22,198 (100.0%)Missing1 (10.0%)4 (40.0%)5 (50.0%) (0.0%)10 (100.0%)TOTAL11,130 (14.7%)29,855 (39.3%)32,949 (43.4%)2,024 (2.7%)75,958 (100.0%)Negative RespondersIMD vs smoking; negative respondersCurrent SmokerEx-smokerNever smokerMissingTOTALQ1 most deprived672 (15.8%)1,771 (41.5%)1,677 (39.3%)145 (3.4%)4,265 (100.0%)Q2359 (10.9%)1,265 (38.3%)1,558 (47.2%)118 (3.6%)3,300 (100.0%)Q3380 (8.7%)1,565 (35.8%)2,270 (51.9%)160 (3.7%)4,375 (100.0%)Q4335 (6.8%)1,754 (35.6%)2,677 (54.3%)167 (3.4%)4,933 (100.0%)Q5 least deprived332 (5.6%)1,904 (32.2%)3,503 (59.2%)174 (2.9%)5,913 (100.0%)Missing (0.0%)1 (50.0%)1 (50.0%) (0.0%)2 (100.0%)TOTAL2,078 (9.1%)8,260 (36.2%)11,686 (51.3%)764 (3.4%)22,788 (100.0%)(x) POPULATION SMOKING FIGURES BY IMD QUINTILEThese figures are derived from the Health Survey for England 2010 (HSE2010), available via the UK Data Service, ukdataservice.ac.uk. Figures relate to the per-IMD quintile percentages for cigarette smoking in n=3246 individuals aged 50-75 years. The age distribution within this sample from the HSE2010 data did not differ significantly from that of the UKLS sample, so a direct comparison was possible.?IMDCurrent smokerEx-smokerNever smokerQ1 most deprived28.2%34.3%37.5%Q222.2%35.5%42.4%Q318.5%38.1%43.4%Q412.8%34.9%52.4%Q5 least deprived8%35.9%56%Appendix 6:Summary Report of UKLS sample QCUKLS samples were randomly selected (within groups chosen to represent each recruitment centre) and subjected to standard biomolecule extraction, quantitation and quality control procedures. In some cases extractions were performed alongside samples from the Liverpool Lung Project Biobank to provide a benchmark.Sample TypeExtractionQC1EDTA Sep remnantsDNA by Qiagen kitSpectrophotometry, agarose gel electrophoresis2Blood cellsDNA by Qiagen kitSpectrophotometry, agarose gel electrophoresis3Blood cellsDNA by Source BioscienceSpectrophotometry, agarose gel electrophoresis4Buccal scrapeDNA by Qiagen kitSpectrophotometry, agarose gel electrophoresis5Nasal scrapeDNA by Qiagen kitSpectrophotometry, agarose gel electrophoresis6Buccal scrapeRNA by Qiagen kitSpectrophotometry,Qbit, Agilent Bioanalyser7Nasal scrapeRNA by Qiagen kitSpectrophotometry,Qbit, Agilent Bioanalyser8PlasmamicroRNA by Norgen kitQuanitative RT-PCR (10 miRNA + RNU48)9SputumCytospinSputum adequacyDNA was extracted for 10 of each of the approx. 4ml remnants of 8ml EDTA Sep tubes after removal of plasma (1) or from single aliquots of cell pellets from approx. 1.5ml of EDTA blood (2). Yields were better for cells than for plasma gel-tube remnants, equating to approx. 45?g DNA per 9ml blood tube (in keeping with expectations) and 3.2?g DNA per 8ml plasma gel-tube. DNA quality as assessed by spectrophotometry (A260 quantitation plus A260/A280 and A260/A230 ratios for DNA quality) was similar for all samples tested, but again gel-tubes gave slightly poorer quality DNA (as often associated with lower yields). DNA integrity, assessed by agarose gel electrophoresis, was equivalent across all samples.Having established a baseline for DNA yield in-house, blood cell samples from 80 individuals were commercially extracted by Source Bioscience (3). Quantitation and QC were performed by Source Biosciences using a spectrophotometric plate reader. Results were analysed in respect to study site (Liverpool or Papworth), study nurse and date of collection. The majority of DNA yields were between 10 and 40 ?g, in keeping with what was expected. There was some evidence of greater yields from Liverpool samples, but there was no difference in DNA quality and no obvious trend for any effect study nurse and date of collection. Buccal (4) and Nasal (5) DNA samples were prepared in parallel from samples stored in Saccomanno’s cytology fixative. In keeping with previous experience of these sampling techniques, there was considerable variation in DNA yield between samples. Whilst 90% of buccal samples gave >20ng DNA (compared to 70% of nasal samples), the mean yield of DNA was greater for nasal samples (2.5 ?g compared to 0.12?g), with a concomitant increase in DNA quality. Therefore whilst the majority of samples would be suitable for PCR-based analysis, nasal samples might be better suited to more DNA hungry discovery-phase investigations.For RNAlater samples of buccal (6) and nasal (7) scrapes, the nasal samples were superior in all measurements. Whereas 6/10 nasal samples gave Q-bit concentrations >10ng/?l (range 15 to 120ng/?l) and RIN values of 6.1-7.8, only 4/10 buccal samples gave similar amounts (range 15 to 69ng/?l) but with lower RIN values of 0-5.9. The proportion of samples with very low yield is similar to those for DNA, indicating that sampling technique rather than storage or extraction may be a factor. The generally poor results for buccal RNA reflect the harsher environment in the buccal cavity for RNA (which is known to be more susceptible that DNA). Some improvement in yield might be achieved with new extraction techniques, but current results favour the use of nasal samples for RNA analysis. RNAlater can also be used for DNA extraction, so this may be a sensible alternative use for the Buccal RNAlater samples.Plasma (8) microRNA was subject to multiplex RT and quantitative PCR for a panel consisting of RNU48 and 10 plasma miRNA previously reported to be expressed in plasma. Mean raw Ct values for UKLS samples were significantly lower (Students T-test) and distributions significantly different (Mann Whitney, MW) for several assays, indicating UKLS samples gave similar, or better, yields of microRNA than LLP samples collected over a similar time frame.Sputum samples (9) have been prepared for cytological evaluation by standard means and are awaiting analysis by an independent cytologist.Appendix 7:Psychosocial TablesTable 33: Comparison of T0 (touch screen) completers and non-completers T0 completers (n=4039) ?n (%) or mean (SD)T0 non-completers (n=22) ?n (%) or mean (SD)Trial armIntervention2018 (50)10 (46)Control2019 (50)8 (36)Not randomised2 (<1)4 (18)SiteLiverpool2033 (50)17 (77)Papworth2006 (50)5 (23)Age67.66 (4.09)66.32 (4.10)GenderMale 3021 (75)20 (91)Female1018 (25)2 (9)Ethnicity White 3985 (99)8 (89)Non-white38 (1)1 (11)Marital status Married /cohabiting 2955 (73)8 (80) Not married/cohabiting*1073 (27)2 (20)IMDQuintile 11079 (27)11 (50)Quintile 2486 (12)1 (5)Quintile 3719 (18)4 (18)Quintile 4 729 (18)3 (14)Quintile 51026 (25)3 (14)Smoking status (UKLSQ1)Current smoker1559 (39)9 (41)Ex-smoker2478 (61)13 (59)Never smoker2 (<1)0 (0)?Ns vary within each cell due to missing data. Percentages are calculated based on available data.*not married/cohabiting = single, widowed, divorced/separated.Table 34: Comparison of baseline CT scan attenders and non-attenders Attended CT scan n=1987??n (%) or mean (SD)Did not attend CT scan n=31?n (%) or mean (SD)SiteLiverpool1001 (50)15 (48)Papworth986 (50)16 (52)Age67.71 (4.03)68.42 (4.85)GenderMale 1498 (75)22 (71)Female489 (25)9 (29)Education^ Up to GCSE/O level or equivalent 667 (46)11 (55)Beyond GCSE/O level or equivalent779 (54)9 (45)Ethnicity White 1961 (99)31 (100)Non-white18 (1)0 (0)Marital status Married /cohabiting 1462 (74)21 (70)Not married/cohabiting* 519 (26)9 (30)IMDQuintile 1537 (27)8 (26)Quintile 2239 (12)4 (13)Quintile 3354 (18)4 (13)Quintile 4 347 (18)6 (19)Quintile 5510 (26)9 (29)Smoking status (UKLSQ1) Current smoker759 (38)13 (42)Ex-smoker1226 (62)18 (58)Never smoker2 (<1)0 (0)Experience of lung cancer (T0)No1153 (58)15 (48)Yes830 (42)16 (52)Cancer distress (T0) +2.17 (0.29)2.22 (0.31)Anxiety (T0) +1.55 (0.71)1.63 (0.60)Depression (T0) +1.30 (0.68)1.43 (0.69)Decision satisfaction (T0)Not very satisfied 1202 (61)26 (84)Very satisfied 781 (39)5 (16)?Ns vary within each cell due to missing data. Percentages are calculated based on available data.*not married/cohabiting = single, widowed, divorced/separated.+log transformed scores.?n=3 were not sent a T1 due to administrative error. ^A substantial amount of data were missing or uninformative for the education variableTable 35: Comparison of baseline screening test result groups on demographic and baseline psychosocial variablesNegative (n=763) ?n (%) or mean (SD)Negative with incidental finding (n=41) ?n (%) or mean (SD)Positive for repeat scan (n=788) ? n (%) or mean (SD)Positive for MDT referral (n=48) ? n (%) or mean (SD)Test statistic (p-value)SiteLiverpool376 (49)25 (61)385 (49)27 (56)χ2(3) =3.2 (.37)Papworth387 (51)16 (39)403 (51)21 (44)Age67.63 (3.94) 67.88 (3.69) 67.76 (3.94) 67.67 (3.95)F(3, 1636) = 0.2 (.91)GenderMale 594 (78)30 (73)581 (74)37 (77)χ2(3) =3.8 (.29)Female169 (22) 11 (27)207 (26) 11 (23)Education^ Up to GCSE/O level or equivalent 228 (42)18 (60)256 (46)9 (29)χ2(3) =7.6 (.06)Beyond GCSE/O level or equivalent315 (58)12 (40)303 (54)22 (71)Ethnicity White 755 (99)40 (98)777 (>99)47 (98)n/aNon-white7 (1) 1 (2) 4 (1)1 (2)Marital status Married /cohabiting 585 (77)25 (61)583 (74)37 (77)χ2(3) = 5.8 (.12)Not married/cohabiting* 177 (23)16 (39)201 (26) 11 (23)IMDQuintile 1204 (27) 11 (27)208 (26)19 (40)n/aQuintile 294 (12)5 (12)90 (11)2 (4)Quintile 3135 (18)7 (17)139 (18)9 (19)Quintile 4 121 (16)7 (17)144 (18)8 (17)Quintile 5209 (27)11 (27)207 (26)10 (21)Smoking status (UKLSQ1) Current smoker275 (36)12 (29)305 (39)23 (48)χ2(3) = 4.6 (.20)Ex-smoker488 (64)29 (71)482 (61)25 (52)Never smoker0 (0)0 (0)1 (<1)0 (0)n/aExperience of lung cancer (T0)No462 (61)24 (59)453 (58)26 (54)χ2(3) = 2.0 (.57)Yes299 (39)17 (42)333 (42)22 (46)Cancer distress (T0) +2.17 (0.28)8.732.19 (0.29)8.932.16 (0.29)8.662.17 (0.32)8.80F(3,1632) = 0.2 (.88)Anxiety (T0) +1.55 (0.68)3.691.43 (0.70)3.191.53 (0.73)3.611.49 (0.72)3.44F(3, 1634) = 0.4 (.73)Depression (T0) +1.29 (0.66)2.631. 19 (0.65)2.281.28 (0.70)2.591.10 (0.70)2.01F(3,1634) = 1.3 (.26)Decision satisfaction (T0) Not very satisfied 445 (58)26 (63)489 (62)31 (65)χ2(3) = 2.8 (.43)Very satisfied317 (42)15 (37)298 (38)17 (35)?Ns vary within each cell due to missing data. Percentages are calculated based on available data.*not married/cohabiting = single, widowed, divorced/separated. +log transformed scores in normal text and original scale scores in italics (analyses performed using log transformed scores).^Note missing data for educationTable 36: T2 sample characteristics by trial allocation Intervention(n =1553) ?n (%) or mean (SD)Control (n = 1302) ?n (%) or mean (SD)Test statistic (p-value)SiteLiverpool735 (47)601 (46)χ2(1) = 0.39 (.53)Papworth818 (53)701 (54)Age67.77 (3.98)67.82 (3.98)t(2853) = 0.04 (.77)GenderMale 1189 (77)977 (75)χ2(1) = 0.90 (.34)Female364 (23)325 (25)Education^ Up to GCSE/O level or equivalent 509 (44)431 (44)χ2(1) = 0.09 (.77)Beyond GCSE/O level or equivalent640 (56)556 (56)Ethnicity White 1536 (99)1289 (99)χ2(1) = 0.03 (.86)Non-white11 (1)10 (1)Marital status Married /cohabiting 1171 (76)1003 (77)χ2(1) = 0.82 (.37)Not married/cohabiting* 377 (24)298 (23)IMDQuintile 1378 (24)290 (22)χ2(4) = 2.61 (.63)Quintile 2186 (12)152 (12)Quintile 3287 (19)236 (18)Quintile 4 286 (18)259 (20)Quintile 5416 (27)365 (28)Smoking status (UKLSQ1)Current smoker568 (37)441 (34)χ2(1) = 2.30 (.13)Ex-smoker984 (63)861 (66)Never smoker1 (<1)0 (0)n/aExperience of lung cancer (T0)No916 (59)767 (59)χ2(1) = 0.001 (.98)Yes634 (41)532 (41)Cancer distress (T0) +2.16 (.28)8.692.15 (.28)8.58t(2845) = -1.24 (.22)Anxiety (T0) +1.52 (.71)3.561.50 (.71)3.47t(2848) = -0.74 (.46)Depression (T0) +1.26 (.67)2.541.25 (.66)2.47t(2845) = -0.71 (.48)Decision satisfaction (T0) Not very satisfied 932 (60)736 (57)χ2(1) = 3.33 (.07)Very satisfied618 (40)561 (43)?Ns vary within each cell due to missing data. +log transformed scores in normal text and original scale scores in italics (analyses performed using log transformed scores).*not married/cohabiting = single, widowed, divorced/separated. ^Note missing data for education.Table 37: Comparison of screening outcome groups (long-term follow-up) on demographic and baseline psychosocial variablesTrue Negative(n=740) ?n (%) or mean (SD)Incidental finding(n=78) ?n (%) or mean (SD)False Positive(n=445) ?n (%) or mean (SD)True Positive(n=23) ?n (%) or mean (SD)Test statistic(p-value)SiteLiverpool348 (47)43 (55)218 (49)14 (61)χ2(3) = 3.5 (.33)Papworth392 (53)35 (45)227 (51) 9 (39)Age67.69 (3.95)67.83 (3.95)67.88 (4.04)67.30 (4.63)F(3, 1282) = 0.3 (.82)GenderMale 584 (79)54 (69)336 (76)19 (83)χ2(3) = 5.2 (.16)Female156 (21)24 (31)109 (25)4 (17)Education^ Up to GCSE/O level or equivalent 229 (41)33 (55)176 (51)8 (47)χ2(3) = 9.7 (.02)Beyond GCSE/O level or equivalent324 (59)27 (45)171 (49)9 (53)Ethnicity White 734 (99)77 (99)436 (99)23 (100)n/aNon-white5 (1)1 (1)5 (1)0 (0)Marital status Married /cohabiting 563 (76)53 (68)336 (76)22 (96)χ2(3) = 7.6 (.06)Not married/cohabiting*177 (24)25 (32)106 (24)1 (4)IMDQuintile 1175 (24)24 (31)112 (25)8 (35)n/aQuintile 294 (13)7 (9)57 (13)3 (13)Quintile 3140 (19)17 (22)69 (16)6 (26)Quintile 4 127 (17)15 (19)86 (19)3 (13)Quintile 5204 (28)15 (19)121 (27)3 (13)Smoking status (UKLSQ1) Current smoker268 (36)30 (39)160 (36)11 (48)χ2(3) = 1.5 (.69)Ex-smoker472 (64)48 (62)285 (64)12 (52)Never smoker0 (0)0 (0)0 (0)0 (0)n/aExperience of lung cancer (T0)No445 (60)52 (67)250 (56)13 (57)χ2(3) = 3.8 (.29)Yes293 (40)26 (33)194 (44)10 (44)Cancer distress (T0) +2.16 (0.27)8.652.20 (0.29)8.992.16 (0.29)8.632.22 (0.31)9.18F(3,1277) = 0.9 (.46)Anxiety (T0) +1.52 (0.69)3.591.38 (0.71)2.971.55 (0.72)3.731.55 (0.79)3.70F(3, 1280) = 1.4 (.24)Depression (T0) +1.27 (0.65)2.551.31 (0.65)2.711. 26 (0.67)2.531.27 (0.60)2.55F(3,1278) = 0.1 (.94)Decision satisfaction (T0) Not very satisfied 435 (59)48 (62)253 (57)13 (57)χ2(3) = 0.7 (.87)Very satisfied304 (41)30 (39)190 (43)10 (44)?Ns vary within each cell due to missing data. Percentages are calculated based on available data.*not married/cohabiting = single, widowed, divorced/separated. +log transformed scores in normal text and original scale scores in italics (analyses performed using log transformed scores).^Note missing data for education.Table 38: Mean baseline cancer distress scores (original scale) by recruitment site and deprivation.LiverpoolN=2030PapworthN=1995IMD Mean (SD)NMean (SD)NQuintile 19.46 (3.32)10099.19 (2.47)68Quintile 29.15 (2.82)3059.29 (2.53)180Quintile 39.07 (2.76)3609.02 (2.59)356Quintile 48.90 (2.63)2499.26 (2.69)477Quintile 58.94 (2.51)1078.14 (2.37)914 ................
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