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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