EXPLAINABLE AI-BASED MULTIPARAMETRIC MRI RADIOMICS FOR PREDICTING CLINICALLY SIGNIFICANT PROSTATE CANCER

Abstract

Background Multiparametric magnetic resonance imaging (mpMRI) is an established component of prostate cancer diagnosis; however, PI-RADS interpretation remains partly subjective, particularly for equivocal lesions. Radiomics can quantify imaging characteristics that may not be readily appreciated visually, while explainable artificial intelligence (XAI) can make machine-learning predictions more transparent. Methods A retrospective cohort of 300 men undergoing prostate mpMRI followed by histopathological assessment was hypothetically evaluated. T2-weighted (T2W), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) sequences were analyzed according to PI-RADS v2.1. Shape, first-order, and texture radiomic features were extracted following standardized preprocessing. Reproducible features were selected using intraclass correlation, correlation filtering, and LASSO. Logistic regression, random forest, support vector machine, and XGBoost models were assessed. SHAP was used to explain feature contributions. Results In the illustrative dataset, 132/300 patients (44.0%) had clinically significant prostate cancer (csPCa; Grade Group ≥2). The integrated PI-RADS, clinical, and radiomics model achieved an illustrative AUC of 0.95 (95% CI, 0.92–0.98), sensitivity of 90.5%, specificity of 88.1%, and accuracy of 89.3% in the test cohort. ADC entropy, T2W texture heterogeneity, ADC percentile measures, DCE texture features, and lesion morphology were among the leading predictors. Conclusions An explainable mpMRI radiomics framework may provide complementary quantitative information to PI-RADS and clinical variables for prediction of csPCa. SHAP-based explanations can link model predictions to anatomically plausible imaging characteristics. Prospective multicenter external validation is required before clinical implementation.