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Abstract #0807

Sorting out PI-RADS 3: A radiologist-assist tool using representation learning to avoid unnecessary biopsies

Lavanya Umapathy1,2, Patricia Johnson1,2, Tarun Dutt1, Angela Tong1, Sumit Chopra1,2,3, Daniel K Sodickson1,2,4, and Hersh Chandarana1,2
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, United States, 4Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, United States

Synopsis

Keywords: Prostate, Prostate, PI-RADS 3, avoiding biopsies, Representational learning, Machine learning

Motivation: Although MRI has a high negative predictive value for low-risk prostate cancers (PCa), it suffers from substantially high-number of false-positives for intermediate-risk cases, resulting in unnecessary biopsies.

Goal(s): To develop an AI-based framework to detect clinically significant PCa (csPCa) in patients diagnosed as intermediate-risk by radiologists.

Approach: We use PI-RADS-guided contrastive learning to generate latent representations from MR images. These serve as a guide to disambiguate PI-RADS3 and provide a tool for identifying potential negative biopsies.

Results: We observe performance comparable to radiologists in identifying csPCa. Improved performance is noted in PI-RADS3 assessments where 10-28% negative biopsies can be avoided with appropriate risk-thresholds.

Impact: Powered with PI-RADS guided representational learning, deep learning models can provide radiologists with additional information to disambiguate intermediate risk PI-RADS3 assessments, avoiding unnecessary biopsies, and potentially helping patient retention in active surveillance protocols.

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