Mp19-17 Integrating MR and Ultrasound Images for AI-Based Prostate Cancer Detection in Transrectal Ultrasound Images: A Comparative Assessment With Clinicians

You have accessJournal of UrologyProstate Cancer: Detection & Screening I (MP19)1 May 2024MP19-17 INTEGRATING MR AND ULTRASOUND IMAGES FOR AI-BASED PROSTATE CANCER DETECTION IN TRANSRECTAL ULTRASOUND IMAGES: A COMPARATIVE ASSESSMENT WITH CLINICIANS Hassan Jahanandish, Sulaiman Vesal, Indrani Bhattacharya, Zachary Kornberg, Steve Ran Zhou, Elijah Richard Sommer, Moon Hyung Choi, Richard E. Fan, Mirabela Rusu, and Geoffrey A. Sonn Hassan JahanandishHassan Jahanandish , Sulaiman VesalSulaiman Vesal , Indrani BhattacharyaIndrani Bhattacharya , Zachary KornbergZachary Kornberg , Steve Ran ZhouSteve Ran Zhou , Elijah Richard SommerElijah Richard Sommer , Moon Hyung ChoiMoon Hyung Choi , Richard E. FanRichard E. Fan , Mirabela RusuMirabela Rusu , and Geoffrey A. SonnGeoffrey A. Sonn View All Author Informationhttps://doi.org/10.1097/01.JU.0001008716.22569.77.17AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: While MRI-guided biopsies have greatly improved prostate cancer detection, most biopsies are still performed using b-mode transrectal ultrasound (TRUS) imaging alone. TRUS biopsy detects just 48% of prostate cancer. Recent studies have focused on machine learning (ML) approaches for detecting prostate cancer in MR images. This limits the deployment of these models to the minority of men who receive a pre-biopsy prostate MRI. To make ML accessible to all men undergoing biopsy, we developed a novel deep learning framework that utilizes biomarkers from both MRI and TRUS images during training to detect prostate cancer foci in TRUS images alone, eliminating the need for MR images in deployment. Additionally, we evaluated our ML model performance against four urologists. METHODS: Our framework comprises a multimodal deep neural network that learns from both MRI and TRUS images, alongside a unimodal network that exclusively uses TRUS images as input both at training time and deployment. The multimodal network was first pre-trained to identify clinically significant prostate cancer (grade group ≥2). Then, this pre-trained model was used to guide the training of the unimodal TRUS-only network. We trained and tested our framework on a dataset of 102 patients (82 training and 20 test cases), with whole-mount pathology as the ground truth. Further, a baseline TRUS-only model was trained using the same dataset with no guidance from the multimodal model. Four urologists, with an average of 5 (±4.8) years of experience reading TRUS prostate images, reviewed the test cohort's TRUS images, manually annotating suspicious lesions without time restrictions. RESULTS: The multimodal-guided TRUS-only model achieved a sensitivity and specificity of 80% and 70%, respectively. This significantly outperformed the unguided baseline model, which achieved a performance of 54% and 48%. Furthermore, expert clinicians achieved a lower sensitivity of 35%, with a higher specificity of 92% compared to our ML approach. CONCLUSIONS: Our results demonstrate the effectiveness of our approach in integrating MRI and TRUS images for prostate cancer detection in TRUS images. The higher sensitivity compared to expert clinicians shows promise for enhancing prostate cancer biopsy diagnosis. Download PPT Source of Funding: Departments of Radiology and Urology, Stanford University, National Cancer Institute of the National Institutes of Health (R37CA260346 to M.R.), 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 © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e317 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Hassan Jahanandish More articles by this author Sulaiman Vesal More articles by this author Indrani Bhattacharya More articles by this author Zachary Kornberg More articles by this author Steve Ran Zhou More articles by this author Elijah Richard Sommer More articles by this author Moon Hyung Choi 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 ...