Refining risk prediction in pediatric acute lymphoblastic leukemia through DNA methylation profiling
Adrián Mosquera Orgueira,
Olga Krali,
Carlos Pérez Míguez
et al.
Abstract:Acute lymphoblastic leukemia (ALL) is the most prevalent cancer in children, and despite considerable progress in treatment outcomes, relapses still pose significant risks of mortality and long-term complications. To address this challenge, we employed a supervised machine learning technique, specifically random survival forests, to predict the risk of relapse and mortality using array-based DNA methylation data from a cohort of 763 pediatric ALL patients treated in Nordic countries. The relapse risk predictor… Show more
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