Improving the Potential for Predicting Prostate Cancer Progression in Patients on Active Surveillance Using Explainable Artificial Intelligence

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Background: Approximately half of prostate cancer (PCa) patients present with low- or intermediate-risk disease eligible for active surveillance (AS). However, a substantial proportion of individuals experience pathological progression during follow-up. In this study, we developed predictive models for histopathological PCa progression in patients on AS. Methods: The dataset comprised patients with biopsy-confirmed PCa and a minimum follow-up of two years. All patients underwent regular surveillance, including prostate-specific antigen (PSA) measurements and MRI examinations. Each patient had three to six consecutive MRI scans available for analysis. Histopathological progression was defined as an upgrade to a higher grade group on repeat targeted biopsy. Predictive modeling integrated radiomic and clinical variables using machine learning (ML). SHapley Additive exPlanations (SHAP) was used for feature interpretation. Results: Three models were obtained: (1) a baseline model utilizing radiomic features from initial MRI scans combined with baseline PSA density (PSAd) (AUC = 0.793, sensitivity = 0.690, specificity = 0.830); (2) a delta model incorporating feature changes between latest and baseline available MRI scans with final PSAd (AUC = 0.913, sensitivity = 0.793, specificity = 0.936); and (3) a time series model analyzing the complete series of radiomic features and PSAd (AUC = 0.917, sensitivity = 0.828, specificity = 0.894). Conclusions: Our predictive models demonstrated strong performance in distinguishing progressors from non-progressors, suggesting that radiomic analysis combined with ML has significant potential to enhance PCa management. This approach could enable more personalized treatment strategies and improve clinical decision-making for patients undergoing AS.

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