PI-RADS Inspired Architecture: A New Deep Learning Approach to Detect Clinically Significant Prostate Cancer on MRI

This work presents PI-RADS-Net, a deep-learning model inspired by the PI-RADS protocol to detect clinically significant prostate cancers (csPCa) on MRI by emulating radiologists' decision-making. The model employs a two-branch architecture, using T2-weighted images for the transition zone and a combination of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) sequences for the peripheral zone. An initial extension, PI-RADS-BimodalNet, which incorporates clinical parameters, was also developed. Experimental results demonstrate that PI-RADS-Net significantly improves classification accuracy, while PI-RADS-BimodalNet achieves an area under the curve (AUC) of 0.83 in testing, surpassing other models. Our approach holds promise for improving the diagnosis of clinically significant prostate cancer, reducing unnecessary biopsies, and enhancing patient outcomes.