PD57-05 A DEEP LEARNING NETWORK ALONG WITH PIRADS CAN DISTINGUISH CLINICALLY SIGNIFICANT AND INSIGNIFICANT PROSTATE CANCER ON BI-PARAMETRIC MRI: A MULTI-CENTER STUDY (2020)

You have accessJournal of UrologyProstate Cancer: Staging II (PD57)1 Apr 2020PD57-05 A DEEP LEARNING NETWORK ALONG WITH PIRADS CAN DISTINGUISH CLINICALLY SIGNIFICANT AND INSIGNIFICANT PROSTATE CANCER ON BI-PARAMETRIC MRI: A MULTI-CENTER STUDY Amogh Hiremath*, Rakesh Shiradkar, Harri Merisaari, Lin Li, Prateek Prasanna, Otta Ettala, Pekka Taimen, Hannu Aronen, Peter Boström, Jonathan Pierce, Sree Harsha Tirumani, Art Rastinehad, Ivan Jambor, Andrei Purysko, and Anant Madabhushi Amogh Hiremath*Amogh Hiremath* More articles by this author , Rakesh ShiradkarRakesh Shiradkar More articles by this author , Harri MerisaariHarri Merisaari More articles by this author , Lin LiLin Li More articles by this author , Prateek PrasannaPrateek Prasanna More articles by this author , Otta EttalaOtta Ettala More articles by this author , Pekka TaimenPekka Taimen More articles by this author , Hannu AronenHannu Aronen More articles by this author , Peter BoströmPeter Boström More articles by this author , Jonathan PierceJonathan Pierce More articles by this author , Sree Harsha TirumaniSree Harsha Tirumani More articles by this author , Art RastinehadArt Rastinehad More articles by this author , Ivan JamborIvan Jambor More articles by this author , Andrei PuryskoAndrei Purysko More articles by this author , and Anant MadabhushiAnant Madabhushi More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000967.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Bi-parametric MRI (bpMRI: T2W MRI and Apparent Diffusion Coefficient maps (ADC) derived from diffusion weighted imaging) is increasingly being used to characterize prostate cancer (PCa). However, inter- and intra- reader variability hinders interpretation of MRI. Recent works have shown that deep learning networks may aid in PCa characterization. In this study, we present a deep multi-instance learning (De-MIL) convolutional neural network (CNN) to distinguish clinically significant PCa (sPCa: gleason grade group (GGG) > 1) and insignificant PCa (iPCa: GGG=1) on bpMRI. METHODS: A retrospective cohort of 359 patients with 492 lesions with data from three sites (site 1:224, 2:147, 3:121 lesions respectively) who underwent 3T bpMRI before prostatectomy or at the time of clinical suspicion was used. Three radiologists from each site annotated cancer regions by looking at prostatectomy sections and/or biopsy reports. Lesions were divided into training set (N = 105), validation set (N = 50) from one site, and test set (N = 337) from all sites. De-MIL (Figure 1a) was trained with T2W, ADC and their corresponding lesion segmentation masks. De-MIL aggregates the representation of patches across a lesion into a pooled representation using multiple instance learning. A logistic regression classifier was trained on combination of De-MIL predictions and Prostate Imaging Reporting and Data System (PI-RADS v2.0/2.1) score to obtain final classification result. RESULTS: The distribution of GGGs of lesions were 1: 158, 2: 171, 3: 78, 4: 22, 5: 63, respectively. Models based on PI-RADS and De-MIL alone resulted in AUC (GGG1 vs >1) of 0.75 and 0.76 respectively, while model trained on combination of De-MIL and PIRADS (MC), yielded AUC of 0.79 (Figure 1b) with significant improvement (t-test, p=0.033, p=0.013 respectively). PI-RADS with cutoff > 3 resulted in sensitivity of 0.85 and specificity of 0.39, while cutoff of 0.5 on MC improved the specificity to 0.64 with a drop in sensitivity to 0.76. De-MIL activation maps in (Figure 1c) show that classifications were derived based on cancerous regions. CONCLUSIONS: A combination of De-MIL and PI-RADS can improve the classification performance in distinguishing sPCa and iPCa compared with PI-RADS alone. Source of Funding: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers 1U24CA199374-01, R01CA202752-01A1R01CA208236-01A1. R01 CA216579-01A1R01 CA220581-01A11U01 CA239055-01 National Center for Research Resources under award number 1 C06 RR12463-01. VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development ServiceThe DoD Breast Cancer Research Program Breakthrough Level 1 Award W81XWH-19-1-0668The DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558)The DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440)The DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329)The Ohio Third Frontier Technology Validation FundThe Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and The Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University. DoD Prostate Cancer Research Program Idea Development Award W81XWH-18-1-0524 © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1195-e1195 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Amogh Hiremath* More articles by this author Rakesh Shiradkar More articles by this author Harri Merisaari More articles by this author Lin Li More articles by this author Prateek Prasanna More articles by this author Otta Ettala More articles by this author Pekka Taimen More articles by this author Hannu Aronen More articles by this author Peter Boström More articles by this author Jonathan Pierce More articles by this author Sree Harsha Tirumani More articles by this author Art Rastinehad More articles by this author Ivan Jambor More articles by this author Andrei Purysko More articles by this author Anant Madabhushi More articles by this author Expand All Advertisement PDF downloadLoading ...