Explainable AI Radiomics in Prostate Cancer Aggressiveness Prediction using different quantitative Diffusion MRI models.

Prostate cancer (PCa) is one of the most frequently diagnosed cancers in men with the average age of diagnosis at 66 years. Accuracy in the early characterization of PCa is the major unmet need in disease management in order to stratify patients with indolent disease or patients with high risk for aggressive disease at an early stage. To this end, a retrospective collection of 202 histopathologically proven PCa patients was explored through quantitative diffusion MRI modelling radiomics to automatically classify Gleason score (GS) between GS<7 and GS≥7 aiming to reduce unnecessary biopsy. The classification was conducted by training a variety of classifiers with T2 and quantitative diffusion MRI data, and the explainability analysis was assessed using the Shapley Additive Explanations (SHAP). The best model in terms of performance was the combination of T2 and the diffusion-derived micro-perfusion fraction parametric map from the intravoxel incoherent motions (IVIM) model, exhibiting a mean accuracy of 80.91% and an AUC of 85.29%. The findings of our work suggest that tissue structural information and blood microperfusion play a significant role in predicting PCa aggressiveness.Clinical Relevance-This work establishes an automated classification of PCa aggressiveness using quantitative diffusion models accompanying T2 weighted images towards reducing the large variability across centers improving the rate of referrals for unnecessary invasive procedures such as biopsy.