MP74-10 DEEP LEARNING FOR SEMI-AUTOMATED PIRADSV2 SCORING ON MULTIPARAMETRIC PROSTATE MRI (2019)

You have accessJournal of UrologyImaging/Radiology: Uroradiology II (MP74)1 Apr 2019MP74-10 DEEP LEARNING FOR SEMI-AUTOMATED PIRADSV2 SCORING ON MULTIPARAMETRIC PROSTATE MRI Tom Sanford*, Stephanie Harmon, Manuel Madariaga, Deepak Kesani, Sherif Mehralivand, Nathan Lay, Sheng Xu, Jonathan Bloom, Amir Lebastchi, Michael Ahdoot, Maria Merino, Brad Wood, Vladimir Valera, Peter Choyke, Peter Pinto, and Baris Turkbey Tom Sanford*Tom Sanford* More articles by this author , Stephanie HarmonStephanie Harmon More articles by this author , Manuel MadariagaManuel Madariaga More articles by this author , Deepak KesaniDeepak Kesani More articles by this author , Sherif MehralivandSherif Mehralivand More articles by this author , Nathan LayNathan Lay More articles by this author , Sheng XuSheng Xu More articles by this author , Jonathan BloomJonathan Bloom More articles by this author , Amir LebastchiAmir Lebastchi More articles by this author , Michael AhdootMichael Ahdoot More articles by this author , Maria MerinoMaria Merino More articles by this author , Brad WoodBrad Wood More articles by this author , Vladimir ValeraVladimir Valera More articles by this author , Peter ChoykePeter Choyke More articles by this author , Peter PintoPeter Pinto More articles by this author , and Baris TurkbeyBaris Turkbey More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557250.08279.36AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Multiparametric MRI (mpMRI) of the prostate has demonstrated great promise in aiding diagnosis of clinically significant prostate cancer. The Prostate Imaging Reporting and Data System (PIRADS)v2 was designed to provide standardized interpretation and reporting but inter-reader agreement remains low. In this study, we use a deep learning (DL) approach to develop a semi-automated PIRADSv2 scoring system. METHODS: Patients undergoing mpMRI for assessment of localized disease from February – October 2018 were considered for study inclusion. mpMRI scans were acquired at 3T with T2-weighted (T2W), diffusion-weighted (ADC map, high b-value DWI acquisition), and dynamic-contrast enhanced (DCE) MRI sequences and were prospectively interpreted by a single expert reader with >10years experience. All detected lesions were manually contoured on T2W MRI with reference to full mpMRI series to capture full extent of lesion boundaries. Bounding boxes derived from lesion contours were fed through a convolutional neural network with a ResNet101 backbone from the fast.ai library as three-channel images (T2W, ADC, high-b). Binary image classification was evaluated comparing PIRADS 2-3 versus PIRADS 4-5. Model performance was further assessed when training with all mpMRI slices encompassing lesion volumes or single mpMRI slice containing largest diameter of each lesion. Data were partitioned to 80% training and 20% validation. Outcome was measured as percent correct classification on the validation dataset. RESULTS: In total, 196 patients with contoured lesions were included in preliminary cohort had a PIRADS score of 2 or higher (n=24 PIRADS 2, n=73 PIRADS 3, n=58 PIRADS 4, n=41 PIRADS 5). When each slice of the tumors were treated as a unique sample, there were a total of 444 segmented tumor slices: n=33 PIRADS 2, n=113 PIRADS 3, n=108 PIRADS 4, n=190 PIRADS 5. Correct classification rate was 60% when the largest slice for each tumor was used. Correct classification rate increased to 71% when all 444 tumor slices were utilized. The PPV, NPV, sensitivity, specificity for correct depiction of PIRADS 4/5 lesions were 87%, 38%, 74%, 58%, respectively. CONCLUSIONS: We have demonstrated the possibility of more accurate and consistent classification of PIRADSv2 scores of lesions detected at mpMRI using an in house-made DL based algorithm. The accuracy of the system showed improvement as the size of the dataset increased in size. Future attempts will focus on further increasing of the dataset size and optimization of the system. Source of Funding: NIH Intramural Research Program. Also, this project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Bethesda, MD; Santiago, Chile; Bethesda, MD© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e1076-e1077 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Tom Sanford* More articles by this author Stephanie Harmon More articles by this author Manuel Madariaga More articles by this author Deepak Kesani More articles by this author Sherif Mehralivand More articles by this author Nathan Lay More articles by this author Sheng Xu More articles by this author Jonathan Bloom More articles by this author Amir Lebastchi More articles by this author Michael Ahdoot More articles by this author Maria Merino More articles by this author Brad Wood More articles by this author Vladimir Valera More articles by this author Peter Choyke More articles by this author Peter Pinto More articles by this author Baris Turkbey More articles by this author Expand All Advertisement PDF downloadLoading ...