Fig. 1

Graphical representation of the study design. The models were trained with data from 763 ALL patients, all of whom had previously been characterized by genome-wide DNA methylation arrays. The dataset was partitioned into a training set (80% of the patients) and a test set (the remaining 20%). The training set was used to identify CpG sites with DNA methylation status associated with two key outcomes: relapse risk and mortality. The selected CpG sites were used to train Random Survival Forests models. Two models were generated: a Relapse Risk Predictor (RRP) and a Mortality Risk Predictor (MRP). The test set was utilized for internal validation. Finally, the models were further validated on two additional datasets