Pd27-03 a Deep Learning Model for Automated Prostate Cancer Detection on Micro-Ultrasound

You have accessJournal of UrologySurgical Technology & Simulation: Artificial Intelligence II (PD27)1 May 2024PD27-03 A DEEP LEARNING MODEL FOR AUTOMATED PROSTATE CANCER DETECTION ON MICRO-ULTRASOUND Steve R. Zhou, Lichun Zhang, Moon Hyung Choi, Sulaiman Vesal, Richard E. Fan, Geoffrey Sonn, and Mirabela Rusu Steve R. ZhouSteve R. Zhou , Lichun ZhangLichun Zhang , Moon Hyung ChoiMoon Hyung Choi , Sulaiman VesalSulaiman Vesal , Richard E. FanRichard E. Fan , Geoffrey SonnGeoffrey Sonn , and Mirabela RusuMirabela Rusu View All Author Informationhttps://doi.org/10.1097/01.JU.0001008580.58088.27.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The Prostate Risk Identification Using Micro-ultrasound (PRIMUS) protocol is validated to help urologists identify lesions for targeted biopsy (TB) without magnetic resonance imaging (MRI). Early studies have shown that micro-ultrasound (MUS)-guided TB has comparable sensitivity to MRI. However, as seen with PI-RADSv2.1, PRIMUS accuracy is likely user- and experience-dependent. The nnUNet is a well-established deep learning semantic segmentation model with successful applications to medical imaging. We therefore sought to automate PCa detection on MUS with an nnUNet 3D model. METHODS: We performed an IRB-approved prospective collection of MUS images from patients undergoing MRI/US-guided biopsy at a single institution. Images consisted of a single sagittal sweep through the prostate which constituted the input to our AI model. All patients received trans-perineal (TP) TB of PI-RADSv2.1 grade≥3 lesions as well as 14-core systematic biopsy (SB). Biopsy-confirmed MRI lesions in T2, DWI, and ADC sequences were co-registered to MUS to help annotate ground truth lesions. We used an nnUNet 3D model for supervised training and five-fold cross-validation for performance evaluation. Prostates were divided into 30 sectors for lesion-level analysis to report sensitivity and specificity. RESULTS: Our dataset included 41 patients with 44 biopsy-confirmed lesions. 7 (17%) patients had a negative biopsy, 34 (83%) patients had PCa, and 29 (71%) had grade group (GG)≥2 PCa. Model sensitivity was 0.77 and specificity was 0.85 for identifying GG≥2 PCa lesions on MUS. Overall accuracy was 0.85. Median Dice coefficient was 0.147 (IQR 0.04-0.34). The nnUnet detected most cancers, but the model also tended to annotate false positives due to imaging artifacts such as shadowing or calcifications (Figure 1). CONCLUSIONS: Our AI model identified PCa lesions on MUS with high sensitivity and specificity. Further work is ongoing to improve margin overlap as evidenced by our Dice coefficient and to perform external validation. Download PPT Source of Funding: None © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e551 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Steve R. Zhou More articles by this author Lichun Zhang More articles by this author Moon Hyung Choi More articles by this author Sulaiman Vesal More articles by this author Richard E. Fan More articles by this author Geoffrey Sonn More articles by this author Mirabela Rusu More articles by this author Expand All Advertisement PDF downloadLoading ...