Survival prediction for Philadelphia chromosome-like acute lymphoblastic leukemia by machine learning analysis: a multicenter cohort study.

ABSTRACT:

Background

This study aimed to develop an efficient survival model for predicting event-free survival (EFS) in patients with Philadelphia chromosome (Ph)-like acute lymphoblastic leukemia (ALL).

Methods

Data related to Ph-like ALL were collected from the South China Children's Leukemia Group (SCCLG) multicenter study conducted from October 2016 to July 2021. A model for predicting the survival of patients with Ph-like ALL was built using Cox proportional hazards regression, random forest, extreme gradient boosting, and gradient boosting machine techniques. By integrating indicators including the concordance index (C-index), 1-, 3-, and 5-year area-under-the-receiver operating characteristics curve (AUROC), Brier score, and decision curve analysis, the predictive capabilities

SUBMITTER: Song XD 

PROVIDER: S-EPMC12488647 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature