Improving PI-RADS rating with Zoomed Diffusion-Weighted Imaging in Deep Learning CAD Systems

Le résumé fourni par la source

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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.