You have accessJournal of UrologyCME1 Apr 2023PD22-03 IMPROVING PROSTATE CANCER DETECTION ON MRI WITH DEEP LEARNING, CLINICAL VARIABLES, AND RADIOMICS Sara Saunders, Xinran Li, Sulaiman Vesal, Indrani Bhattacharya, Simon J. C. Soerensen, Richard E. Fan, Mirabela Rusu, and Geoffrey A. Sonn Sara SaundersSara Saunders More articles by this author , Xinran LiXinran Li More articles by this author , Sulaiman VesalSulaiman Vesal More articles by this author , Indrani BhattacharyaIndrani Bhattacharya More articles by this author , Simon J. C. SoerensenSimon J. C. Soerensen More articles by this author , Richard E. FanRichard E. Fan More articles by this author , Mirabela RusuMirabela Rusu More articles by this author , and Geoffrey A. SonnGeoffrey A. Sonn More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003295.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Radiologists often struggle to differentiate prostate cancer from benign confounders on MRI (i.e. BPH nodules, focal atrophy, prostatitis), but deep learning models may assist clinicians. These models demonstrate high sensitivity in cancer detection but are often limited by high false positive rates. Accurately identifying cancers while minimizing false positives is critical to preventing unnecessary biopsies. We trained classification models on clinical variables and radiomic features to improve differentiation between prostate cancer and benign confounders. METHODS: We trained two state-of-the-art deep learning prostate cancer detection models (SPCNet and U-Net) to identify lesions on MRI in the publicly available PI-CAI dataset consisting of 1500 cases. The SPCNet and U-Net predicted prostate cancer lesions from T2WI, Apparent Diffusion Coefficient (ADC), and Diffusion Weighted Images (DWI). We compared these predictions to pathology confirmed annotations. To identify falsely detected lesions, we trained a Random Forest Classifier on clinical variables (age, PSA, and prostate volume) and radiomic features (i.e. lesion shape, intensity). Once trained on 90% of these cases, it was evaluated on 10%. RESULTS: The U-Net and SPCNet predicted cancerous lesions with positive predictive values of 0.12 and 0.07. Our approach removed 40% of lesions falsely detected by the U-Net and 56% of those falsely detected by the SPCNet while maintaining high sensitivities of 1.0 and 0.97, respectively, compared to all lesions predicted by the cancer detection models. Even more false positive lesions can be removed (71% for U-Net, 74% for SPCNet) at a sensitivity of 0.91. At the patient level, this Random Forest Classifier eliminated 55% of non-cancerous cases but also 15% of true cancer cases predicted by the U-Net. Similar trends were seen for SPCNet. CONCLUSIONS: While clinical variables are routinely used in prostate cancer diagnosis, most deep learning prostate cancer detection models do not utilize them. A classification algorithm trained on clinical variables and radiomic features improves accuracy of cancer detection models by discarding false positive lesions. Source of Funding: Stanford Cancer Imaging Training Program, Departments of Radiology and Urology, Stanford University, National Cancer Institute of the National Institutes of Health (R37CA260346 to M.R, and U01CA196387 to J.D.B.), and the generous philanthropic support of our patients (G.S.) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e665 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sara Saunders More articles by this author Xinran Li More articles by this author Sulaiman Vesal More articles by this author Indrani Bhattacharya More articles by this author Simon J. C. Soerensen More articles by this author Richard E. Fan More articles by this author Mirabela Rusu More articles by this author Geoffrey A. Sonn More articles by this author Expand All Advertisement PDF downloadLoading ...
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