Real-time prostate cancer risk stratification and scan tailoring using deep learning on abbreviated prostate MRI: A prospective evaluation

Motivation: MRI is valuable for detecting and managing Prostate Cancer (PCa), but its use is limited by long scan times. While DCE helps with staging and biopsy guidance, its value only applies if PCa is present. Goal(s): We aim to develop a DL model that identifies csPCa from bpMRI scans in real-time, determining whether DCE is needed. Approach: We trained a DL model using bpMRI to provide feedback directly at the MRI scanner, guiding the need for further imaging. Results: In a prospective test, the model achieved an AUC of 0.86 for PI-RADS ≥ 3. Sensitivity and specificity for csPCa were 0.92 and 0.47. Impact: This study demonstrates that a DL model can guide the selective use of mpMRI based on bpMRI, optimizing resources. This approach could streamline PCa screening, improve patient care, and inspire further research into adaptive and personalized MRI protocols.