Abstract

Motivation: The clinical accuracy of the Prostate Imaging Reporting and Data System (PI-RADS) rating by deep-learning-based computer-aided diagnosis (DL-CAD) models need further enhancement for improved prostate cancer (PCa) detection and fewer unnecessary biopsies. Goal(s): This study aimed to achieve more precise PI-RADS rating for PCa lesions by using zoomed diffusion-weighted imaging (z-DWI) in DL-CAD models. Approach: We compared the diagnostic performance and PI-RADS rating of DL-CAD using advanced z-DWI vs. conventional DWI and extended this analysis to radiological practice. Results: z-DWI improved the PI-RADS rating of PCa lesions by DL-CAD based on superior diagnostic performance compared with conventional DWI. Impact: Deep-learning-based computer-aided diagnosis using zoomed diffusion-weighted imaging provides more accurate PI-RADS rating than conventional DWI, correlating MRI-detected lesions with prostate cancer (PCa) from biopsy. This can help minimize unnecessary biopsies for benign lesions while facilitating timely PCa treatment.