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. 2025 May 2:220:115367.
doi: 10.1016/j.ejca.2025.115367. Epub 2025 Mar 15.

Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences

Gregor Duwe  1 , Dominique Mercier  2 , Verena Kauth  3 , Kerstin Moench  3 , Vikas Rajashekar  2 , Markus Junker  2 , Andreas Dengel  2 , Axel Haferkamp  3 , Thomas Höfner  4
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Free article

Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences

Gregor Duwe et al. Eur J Cancer. .
Free article

Abstract

Background: Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC). Artificial intelligence (AI) could increase evidence-based treatment by generating additional treatment recommendations (TR). We aimed to develop such an AI system for urothelial carcinoma (UC) and renal cell carcinoma (RCC).

Methods: Comprehensive data of patients with histologically confirmed UC and RCC who received MCC recommendations in the years 2015 - 2022 were transformed into machine readable representations. Development of a two-step process to train a classifier to mimic TR was followed by identification of superordinate and detailed categories of TR. Machine learning (CatBoost, XGBoost, Random Forest) and deep learning (TabPFN, TabNet, SoftOrdering CNN, FCN) techniques were trained. Results were measured by F1-scores for accuracy weights.

Results: AI training was performed with 1617 (UC) and 880 (RCC) MCC recommendations (77 and 76 patient input parameters). The AI system generated fully automated TR with excellent F1-scores for UC (e.g. 'Surgery' 0.81, 'Anti-cancer drug' 0.83, 'Gemcitabine/Cisplatin' 0.88) and RCC (e.g. 'Anti-cancer drug' 0.92 'Nivolumab' 0.78, 'Pembrolizumab/Axitinib' 0.89). Explainability is provided by clinical features and their importance score. Finally, TR and explainability were visualized on a dashboard.

Conclusion: This study demonstrates for the first time AI-generated, explainable TR in UC and RCC with excellent performance results as a potential support tool for high-quality, evidence-based TR in MCC. The comprehensive technical and clinical development sets global reference standards for future AI developments in MCC recommendations in clinical oncology. Next, prospective validation of the results is mandatory.

Keywords: Artificial intelligence; Clinical oncology; Deep learning; Digital oncology; Genitourinary cancer; Machine learning; Multidisciplinary cancer conferences; Renal cell carcinoma; Treatment recommendation; Urothelial cell cancer.

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Conflict of interest statement

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gregor Duwe: reports no serving advisory role; receiving honoraria or travel expenses from Johnson & Johnson Corporation, Merck KGaA and Boston Scientific Corporation; receiving grant from the German Federal Ministry of Education and Research (BMBF, grant number: 16SV9053). Dominique Mercier: reports no serving advisory role, honoraria or travel expenses; receiving grant from the German Federal Ministry of Education and Research (grant number: 16SV9053). Verena Kauth: reports no serving advisory role; receiving honoraria or travel expenses from LAWG Deutschland e.V.; receiving grants or funds from Sanofi-Aventis Deutschland GmbH; receiving grant from the German Federal Ministry of Education and Research (grant number: 16SV9053). Kerstin Moench: reports no serving advisory role, honoraria or travel expenses; receiving grant from the German Federal Ministry of Education and Research (grant number: 16SV9053). Vikas Rajashekar: reports no serving advisory role, honoraria or travel expenses; receiving grant from the German Federal Ministry of Education and Research (grant number: 16SV9053). Markus Junker: reports no serving advisory role, honoraria or travel expenses; receiving grant from the German Federal Ministry of Education and Research (grant number: 16SV9053). Axel Haferkamp: reports no serving advisory role; receiving honoraria or travel expenses from Astellas Pharma Inc. and Ipsen Pharma; receiving grant from the German Federal Ministry of Education and Research (grant number: 16SV9053). Andreas Dengel: reports no serving advisory role, honoraria or travel expenses; receiving grant from the German Federal Ministry of Education and Research (grant number: 16SV9053). Thomas Höfner: reports serving advisory roles for Pfizer, Astra-Zeneca, Astellas Pharma Inc., MSD Bristol Myers Squibb; receiving honoraria or travel expenses from Pfizer, Astra-Zeneca, MSD, Johnson & Johnson Corporation and Ferring; receiving grant from the German Federal Ministry of Education and Research (grant number: 16SV9053).

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