Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care
With this proof-of-concept, we integrated RWD into predictive modeling using ML techniques to predict clinical outcomes and explore their potential implications for clinical decision making. The potential of XAI was demonstrated to enhance trust and improve the usability of the model in clinical settings. Further research, including foundation modeling and generative AI, will likely increase the predictive power of prognostic and predictive ML models in advanced melanoma.
Authors
- Gabriel Paardekooper
- Jan Gerard Maring (ORCID: https://orcid.org/0009-0009-8533-9233)
- Jan Willem B. de Groot (ORCID: https://orcid.org/0000-0002-1043-5011)
- F. Lijffijt
- Joep Dille
- Richard M. Brohet (ORCID: https://orcid.org/0000-0003-4472-9322)
- Luuk H.W. Idzerda
- Elianne C.S. de Boer (ORCID: https://orcid.org/0009-0002-4314-5106)
- Joram M. Mossink
- Joni J.N. van der Eerden
- Alexander Oostmeyer
- Michel Beld
Institutions
- Isala (NL)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1200/cci-24-00181
- Citations
- 5
- Primary Topic
- Mathematical Biology Tumor Growth
- Type
- article
- Field-Weighted Citation Impact
- 6.84