PDF Genitourinary Radiology

Genitourinary Radiology

Program subject to change until 12/16/2019.

UR001-EB-X

Imaging Associations in Male Infertility

All Day Room: NA Hardcopy Backboard

Participants

Berenice Lopez Leal, MD, Staten Island, NY (Presenter) Nothing to Disclose Daniel M. Eisman, MD, Staten Island, NY (Abstract Co-Author) Nothing to Disclose Rachel Saks, BS, Rego Park, NY (Abstract Co-Author) Nothing to Disclose Brent Cham, BS,MS, Tampa, FL (Abstract Co-Author) Nothing to Disclose Steven Peti, MD, Staten Island, NY (Abstract Co-Author) Nothing to Disclose

For information about this presentation, contact:

blopezleal@northwell.edu

TEACHING POINTS

Familiarize the radiologist on common male etiologies of infertility Protocols for male infertility imaging Imaging examples of causes of male infertility

TABLE OF CONTENTS/OUTLINE

Approximately 15% to 20% of couples experience infertility at some point, with adverse effects having psychologic, social, and economic implications. It is estimated that 30% of infertility cases can be attributed solely to male factors and up to 50% of all cases involve a male component. Identification of male factors in infertility, some of which are reversible, can strongly impact couples experiencing infertility and can be a critical component in further decision making. Diagnostic imaging provides a noninvasive assessment of the male genital tract, allowing physicians to potentially identify the underlying etiology in cases of infertility. Familiarity with the associated findings allow the radiologist to provide a more detailed and targeted report, and the ability to positively impact patient care. 1. Testicular Etiologies a. Varicocele b. Cryptorchidism c. Testicular insult - infarction, trauma, tumor, post-therapy 2. Extra-testicular Etiologies a. Congenital bilateral absence of vas deferens/seminal vesicles b. Seminal vesicle cysts c. Prostatic cysts d. Ejaculatory duct/epididymal obstruction

Printed on: 10/30/19

UR002-EB-X

Automatic Quantitative Analysis of Kidney Tumor Using 3D Fully Convolutional Network

All Day Room: NA Hardcopy Backboard

FDA Discussions may include off-label uses.

Participants

Chenglong Wang, Nagoya, Japan (Abstract Co-Author) Nothing to Disclose Masahiro Oda, PhD, Nagoya, Japan (Abstract Co-Author) Nothing to Disclose Yuichiro Hayashi, PhD, Nagoya, Japan (Abstract Co-Author) Nothing to Disclose Naoto Sassa, MD, Nagoya, Japan (Abstract Co-Author) Nothing to Disclose Tokunori Yamamoto, Nagoya, Japan (Abstract Co-Author) Nothing to Disclose Kensaku Mori, PhD, Nagoya, Japan (Presenter) Developer, Olympus Corporation; Developer, Cybernet System Inc; Developer, Morita Mfg Inc

TEACHING POINTS

The purpose of this exhibit is To learn fully-automated segmentation of the kidneys and kidney tumors from CT volume To learn fully-automated quantitative analysis method for kidney tumors To demonstrate deep learning-based analysis system for kidney tumors To show the internal relations between tumor morphology and treatment plan

TABLE OF CONTENTS/OUTLINE

Importance of pre-operative CT image diagnosis in partial nephrectomy How kidney tumor's morphology affects treatment plan What can our assistance system do? Accurate kidney region and kidney tumor segmentation on CT image Extraction of kidney region and kidney tumors using 3D fully convolutional network Quantitative analysis of kidney tumors Analysis of relationship between tumor morphology and treatment Clinical application Deeper insight into relationship between kidney tumors and their treatment More standardized surgical plan for nephrectomy Demonstrate our computer-aided system Fully automated kidney and kidney tumor segmentation (Fig. 1) Calculation of statistical measures of tumors (Fig. 2 and 3) Interactive demonstration of results in 3D rendering and 3D printed model

Printed on: 10/30/19

UR003-EB-X

MRI and Transrectal US Findings 12 Months After MRI-guided Transurethral Ultrasound Ablation (MRITULSA) for Localized Prostate Cancer

All Day Room: NA Hardcopy Backboard

Participants

Kevin A. Eng, MD, Waterloo, ON (Presenter) Nothing to Disclose Joseph Chin, MD, London, ON (Abstract Co-Author) Nothing to Disclose Shiva Nair, London, ON (Abstract Co-Author) Nothing to Disclose Gary L. Brahm, BMedSc, MD, London, ON (Abstract Co-Author) Nothing to Disclose Derek W. Cool, MD,PhD, London, ON (Abstract Co-Author) Nothing to Disclose

TEACHING POINTS

Prostate cancer is the most commonly diagnosed cancer and the second most common cause of cancer death among men in the United States. Due to widespread use of prostate-specific antigen screening, most cases are localized when diagnosed. Standard treatments for localized disease include active surveillance, radiation therapy, or radical prostatectomy. Ablation with cryotherapy or ultrasound (US) may be potential alternatives. Magnetic resonance imaging-guided transurethral ultrasound ablation (MRI-TULSA) is a novel minimally invasive technique that uses real-time MR-thermometry to guide transurethral ablation of prostate tissue with an ultrasound applicator. Unlike high intensity focused US or cryotherapy, MRI-TULSA performs whole gland ablation that drastically alters the appearance of the prostate on MRI and transrectal US, making post-treatment cancer surveillance challenging. This educational abstract provides an overview of MRI-TULSA and demonstrates the spectrum of imaging findings seen on MRI and transrectal US 12 months post-procedure.

TABLE OF CONTENTS/OUTLINE

A) Overview of MRI-TULSA B) Case 1: Complete ablation on MRI, two subcentimeter hypoechoic lesions on US C) Case 2: Nonenhancing hypointense (T2) lesion on MRI D) Case 3: Enhancing hypointense (T2) lesion on MRI E) Case 4: Enhancing hypointense (T2) lesion on MRI

Printed on: 10/30/19

UR004-EB-X

Uncommon Urinary Bladder Tumors: Different Imaging Features for Urothelial Carcinoma

All Day Room: NA Hardcopy Backboard

Participants

Yong-soo Kim, MD, PhD, Guri, Korea, Republic Of (Presenter) Nothing to Disclose Youngseo Cho, MD, Seoul, Korea, Republic Of (Abstract Co-Author) Nothing to Disclose On-Koo Cho, MD, PhD, Seoul, Korea, Republic Of (Abstract Co-Author) Nothing to Disclose

For information about this presentation, contact:

ysookim@hanyang.ac .kr

TEACHING POINTS

1. Understanding classification of bladder tumors and frequency. 2. Characterization of image feature correlated with cystoscopic and pathological features.

TABLE OF CONTENTS/OUTLINE

We describe the characteristic features about bladder tumors with cystoscopic, pathological and imaging features, following details. 1. Epithelial tumors 1) Urothelial carcinoma 2) squamous cell carcinoma 3) adenocarcinoma 4) small cell carcinoma 5) lymphoma 2. Nonepithelial tumor 1) leiomyoma 2) metastasis 3) lymphoma as secondary involvement 3. bladder tumor mimicking lesion 1) actinomycosis 2) Cystitis cystica glandularis Printed on: 10/30/19

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