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

被引:2
作者
Brohet, Richard M. [1 ]
de Boer, Elianne C. S. [2 ]
Mossink, Joram M. [1 ]
van der Eerden, Joni J. N. [1 ]
Oostmeyer, Alexander [1 ]
Idzerda, Luuk H. W. [1 ]
Maring, Jan Gerard [3 ]
Paardekooper, Gabriel M. R. M. [4 ]
Beld, Michel [5 ]
Lijffijt, Fiona [6 ]
Dille, Joep [7 ]
de Groot, Jan Willem B. [2 ]
机构
[1] Isala, Dept Innovat & Sci, Div Data Sci, Zwolle, Netherlands
[2] Isala, Dept Oncol Ctr, Zwolle, Netherlands
[3] Isala, Dept Clin Pharm, Zwolle, Netherlands
[4] Isala, Dept Radiotherapy, Zwolle, Netherlands
[5] Isala, Dept Business Intelligence, Zwolle, Netherlands
[6] Isala, Dept Med Eth & Legal Affairs, Zwolle, Netherlands
[7] Isala, Dept Innovat & Sci, Zwolle, Netherlands
关键词
METASTATIC MELANOMA; IMMUNOTHERAPY; SURVIVAL; OUTCOMES; THERAPY; MODELS;
D O I
10.1200/CCI-24-00181
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
PURPOSEThe use of real-world data (RWD) in oncology is becoming increasingly important for clinical decision making and tailoring treatment. Despite the significant success of targeted therapy and immunotherapy in advanced melanoma, substantial variability in clinical responses to these treatments emphasizes the need for personalized approaches to therapy.MATERIALS AND METHODSIn this pilot study, 239 patients with melanoma were included to predict the response to both targeted therapies and immunotherapies. We used machine learning (ML) to incorporate RWD and applied explainable artificial intelligence (XAI) to explain the individual predictions.RESULTSWe developed, validated, and compared four ML models to evaluate 2-year survival using RWD. Our research showed encouraging outcomes, achieving an AUC of more than 80% and an estimated accuracy of over 74% across the four ML models. The random forest model exhibited the highest performance in predicting 2-year survival with an AUC of 0.85. Local interpretable model-agnostic explanations was used to explain individual predictions and provide trust and insights into the clinical implications of the ML model.CONCLUSIONWith 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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页数:12
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