Safe AI for Prostate Cancer Diagnosis

Safe prostate cancer segmentation and detection.

Abstract: Advances in natural language processing can build on image processing breakthroughs to offer clinicians new AI tools against prostate cancer (PCa). Current AI for interpreting mp-MRI scans relies on visual encodings like lesion annotations but fails at translation in patient care because it doesn't use the standardized PI-RADS (Prostate Imaging Reporting and Data System) format accepted by clinicians. The expertise in PI-RADS reports offers a major resource for training AI to achieve clinical acceptance. This research addresses two gaps: (1) Data availability—public PCa data repositories lack PI-RADS reports, and (2) AI modeling—existing approaches can't integrate complex radiologist expertise expressed through language. We will test the hypothesis that PI-RADS reports can be made machine-readable and combined with visual data so that AI can be trained to interpret MRIs according to the reasoning processes of radiologists. University of Delaware researchers and Memorial Sloan Kettering radiologists will collaborate to develop datasets and tools for safe AI-assisted prostate MRI interpretation.