A machine learning algorithm for automatic tumour board recommendations in prostate cancer patients
This study demonstrates the potential of ML to replicate MTB decision patterns in prostate cancer with reasonable accuracy. However, the current model requires further optimization before it can be considered for clinical application. It should be regarded as a proof-of-concept that highlights both the opportunities and the challenges of algorithm-based decision support in oncology. Future work should focus on improving model performance through multi-institutional data, prospective validation and continuous adaptation to evolving clinical guidelines.
Authors
- Matthias Miederer (ORCID: https://orcid.org/0000-0001-5075-5995)
- Tobias Hölscher (ORCID: https://orcid.org/0000-0002-9991-6152)
- Anke Rentsch
- Roman Herout (ORCID: https://orcid.org/0000-0002-9634-2632)
- Marcus Sondermann (ORCID: https://orcid.org/0000-0002-4629-0707)
- Fabian Lohaus (ORCID: https://orcid.org/0000-0003-4043-7066)
- Katharina Böehm (ORCID: https://orcid.org/0000-0002-0527-1809)
- Sherif Mehralivand (ORCID: https://orcid.org/0000-0003-1405-3683)
- Fabian Funer
- Christian Thomas
- Hugh Glaser
Institutions
- Philipps University of Marburg (DE)
- University Hospital Carl Gustav Carus (DE)
Publication Details
- Journal
- BJUI Compass
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/bco2.70066
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Astellas Pharma