Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences
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.
Authors
- Dominique Mercier (ORCID: https://orcid.org/0000-0001-8817-2744)
- Gregor Duwe (ORCID: https://orcid.org/0000-0001-7008-1238)
- Thomas Höfner
- Vikas Rajashekar (ORCID: https://orcid.org/0000-0002-3664-5156)
- Kerstin Moench
- Axel Haferkamp
- Verena Kauth
- Andreas Dengel (ORCID: https://orcid.org/0000-0002-6100-8255)
- Markus Junker
Institutions
- Johannes Gutenberg University Mainz (DE)
- German Research Centre for Artificial Intelligence (DE)
- University Medical Center of the Johannes Gutenberg University Mainz (DE)
- Krankenhaus der Elisabethinen (AT)
Publication Details
- Journal
- European Journal of Cancer
- Published
- 2025-03-15
- DOI
- https://doi.org/10.1016/j.ejca.2025.115367
- Citations
- 10
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 13.60
Funders
- Bristol-Myers Squibb
- Pfizer
- Sanofi
- Boston Scientific Corporation
- Merck KGaA
- Bundesministerium für Bildung und Forschung
- Astellas Pharma
- Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie
- Ipsen
- Sanofi-Aventis Deutschland