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.

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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
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article

Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care

Gabriel Paardekooper, Jan Gerard Maring, Jan Willem B. de Groot, F. Lijffijt et al.
5 citations
JCO Clinical Cancer Informatics
Mathematical Biology Tumor Growth
6.84
article

Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care

Gabriel Paardekooper, Jan Gerard Maring, Jan Willem B. de Groot, F. Lijffijt, Joep Dille, Richard M. Brohet, Luuk H.W. Idzerda, Elianne C.S. de Boer, Joram M. Mossink, Joni J.N. van der Eerden, Alexander Oostmeyer, Michel Beld
article en
5 citations

Abstract

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.

JCO Clinical Cancer InformaticsVol. 9(9)
Isala (NL)
Peace, Justice and strong institutions
Openalex Percentile: Top 3%
Mathematical Biology Tumor Growth
6.84
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Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care — Gabriel Paardekooper, Jan Gerard Maring, et al. · JCO Clinical Cancer Informatics (2025) | TGRS Research Map | TGRS