AI‐driven preoperative risk assessment in kidney cancer surgery: A comparative feasibility study of machine learning models
The ML models provide valuable information for preoperative risk stratification of patients undergoing renal tumour surgery. This study suggests that NNs are the most appropriate models to stratify patients regarding the occurrence of MCs and AKIs, respectively. The models are made publicly available for reproducibility.
Authors
- Julia Mühlbauer (ORCID: https://orcid.org/0000-0002-0503-0925)
- Lena Maier‐Hein (ORCID: https://orcid.org/0000-0003-4910-9368)
- Evangelia Christodoulou (ORCID: https://orcid.org/0000-0001-7900-5952)
- Alexander Studier‐Fischer (ORCID: https://orcid.org/0000-0001-8682-9300)
- Karl‐Friedrich Kowalewski (ORCID: https://orcid.org/0000-0003-2931-6247)
- Giovanni Cacciamani (ORCID: https://orcid.org/0000-0002-8892-5539)
- Luisa Egen (ORCID: https://orcid.org/0000-0002-9298-3715)
- Keno März (ORCID: https://orcid.org/0000-0003-3503-1918)
- Stephan Maurice Michel
- Caelán Max Haney (ORCID: https://orcid.org/0000-0002-4209-9470)
- Allison Quan
- Luise Ingvelde Monika Gottstein
Institutions
- University of Southern California (US)
- German Cancer Research Center (DE)
- Queen's University (CA)
- Heidelberg University (DE)
- Keck Hospital of USC (US)
- University Hospital Heidelberg (DE)
- Helmholtz Zentrum München (DE)
- National Center for Tumor Diseases (DE)
- University Medical Centre Mannheim (DE)
Publication Details
- Journal
- BJUI Compass
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1002/bco2.70080
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65