Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels

Multidisciplinary cancer conferences (MCC) provide an expert platform for evaluating and selecting the best possible oncological treatment options. Artificial intelligence (AI) can be a highly promising technology to complement additional treatment recommendations (TR). In this context, we developed an AI system supporting prostate cancer (PC) TRs. Data from PC patients receiving MCC recommendations (2015-2022) were converted into a machine-readable format to train classifiers replicating TRs using machine learning and deep learning techniques. A two-step process identified superordinate recommendation categories (high-level) before specifying detailed TR (low-level). AI training was performed on 5478 MCC cases (76 patient input and 23 output parameters). The AI system generated fully automated TR with excellent F1-scores for high-level (e.g. surgery (0.89), radiation therapy (0.81)) and low-level (e.g. prostatectomy (0.99), PSMA-Ligand therapy (0.98)). Explainability is provided by clinical features and their importance score. To our knowledge, this study presents the first AI-generated explainable TR in metastatic and non-metastatic PC with multi-target and feature space, achieving excellent performance results.