mpMRI-based interpretable machine learning model for predicting castration-resistant prostate cancer risk

Androgen deprivation therapy (ADT) efficacy exhibits significant interindividual heterogeneity in advanced prostate cancer (PCa). This study aim to develop and validate a machine learning model based on multiparametric MRI (mpMRI) to predict the risk of Castration-Resistant Prostate Cancer (CRPC) progression in advanced PCa.

In this retrospective study, 180 advanced PCa patients who underwent mpMRI before ADT were collected from two hospitals between January 2014 and October 2024. Radiomic features were selected by using variance threshold, Least Absolute Shrinkage and Selection Operator and recursive feature elimination. Random forest and logistic regression model selected clinical features. We developed eight machine learning classifiers, a stacking ensemble model by integrating the optimally performing classifier. Model performance was evaluated using confusion matrices, accuracy, precision, recall, F1-score, and area under curve (AUC). SHapley Additive exPlanations was employed for both global and local model interpretability. Intergroup differences were analyzed using one-way Analysis of Variance or Kruskal-Wallis tests.

180 patients (mean age 72, range 51-90 years) were stratified by the time to CRPC into groups: very high-risk (<1 year, n = 49), high-risk (1-4 years, n = 65) and low-risk (>4 years, n = 66). The mpMRI-clinical combined model performed better than mpMRI-alone model (AUC:0.84;95 %CI:0.73, 0.92).The stacking model further enhanced CRPC prediction, internal test set (AUC: 0.89; 95 %CI:0.81, 0.93) and external test set (0.82; 95 %CI:0.72, 0.89). In low-risk group, stacking model demonstrated the strongest discriminative capability AUC (0.89;95 %CI:0.77, 0.97).

The Stacking model demonstrated favorable predictive capability for CRPC progression risk in advanced Pca, facilitating clinically actionable risk-stratified interventions.