European journal of cancer (Oxford, England : 1990) · 2026
Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels.
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Duwe Gregor, Mercier Dominique, Kauth Verena, Jost Lisa Maria, Moench Kerstin, Rajashekar Vikas, Junker Markus, Neumann Christopher C M, Dengel Andreas, Haferkamp Axel, Höfner Thomas
Аннотация
BACKGROUND: Therapeutic decisions in clinical oncology are commonly established through interdisciplinary consensus in multidisciplinary cancer conferences (MCC). Artificial intelligence (AI) may support these processes by generating data-driven treatment recommendations (TR). We developed and evaluated an explainable AI system designed to reproduce MCC-based treatment decisions for metastatic and non-metastatic prostate cancer (PC).
METHODS: Clinical data from patients with histologically confirmed PC discussed in MCC between 2015 and 2022 were transformed into structured datasets. A hierarchical modeling framework was implemented to first predict overarching treatment categories and subsequently specify therapeutic strategies. Multiple machine learning and deep learning algorithms were trained to replicate MCC recommendations. Model performance was assessed using F1-scores.
RESULTS: A total of 5478 MCC cases including 76 clinical input variables and 23 treatment output parameters were analyzed. The AI system generated automated TR with high predictive accuracy across both hierarchical levels. For high-level categories, F1-scores reached 0.89 for surgery and 0.81 for radiation therapy. For detailed recommendations, F1-scores reached 0.99 for prostatectomy and 0.98 for PSMA-ligand therapy. Lower performance in anti-cancer drug categories likely reflects smaller sample sizes. Feature importance analyses ensured model transparency and interpretability.
CONCLUSION: To our knowledge, this study presents one of the first large-scale explainable AI system capable of generating MCC-aligned treatment recommendations for metastatic and non-metastatic PC within a multi-target framework. It incorporates the largest reported number of clinical input and treatment output parameters in this setting. Strong predictive performance and interpretability support its potential as a scalable decision-support tool in multidisciplinary oncology. Prospective validation is warranted.
METHODS: Clinical data from patients with histologically confirmed PC discussed in MCC between 2015 and 2022 were transformed into structured datasets. A hierarchical modeling framework was implemented to first predict overarching treatment categories and subsequently specify therapeutic strategies. Multiple machine learning and deep learning algorithms were trained to replicate MCC recommendations. Model performance was assessed using F1-scores.
RESULTS: A total of 5478 MCC cases including 76 clinical input variables and 23 treatment output parameters were analyzed. The AI system generated automated TR with high predictive accuracy across both hierarchical levels. For high-level categories, F1-scores reached 0.89 for surgery and 0.81 for radiation therapy. For detailed recommendations, F1-scores reached 0.99 for prostatectomy and 0.98 for PSMA-ligand therapy. Lower performance in anti-cancer drug categories likely reflects smaller sample sizes. Feature importance analyses ensured model transparency and interpretability.
CONCLUSION: To our knowledge, this study presents one of the first large-scale explainable AI system capable of generating MCC-aligned treatment recommendations for metastatic and non-metastatic PC within a multi-target framework. It incorporates the largest reported number of clinical input and treatment output parameters in this setting. Strong predictive performance and interpretability support its potential as a scalable decision-support tool in multidisciplinary oncology. Prospective validation is warranted.