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 of each model were compared.

Results: The random forest algorithm demonstrated the most robust predictive performance. In the test set, the C-index of the random forest model was 0.797 (95% CI: 0.736-0.821; P < 0.001). The AUROCs for 1, 3, and 5 years were 0.787 (95% CI: 0.62-0.953; P < 0.001), 0.797 (95% CI: 0.589-1; P < 0.001), and 0.861 (95% CI: 0.606-1; P < 0.001), respectively. The Brier scores for 1, 3, and 5 years were 0.102 (95% CI: 0.032-0.173; P < 0.001), 0.126 (95% CI: 0.063-0.19; P < 0.001), and 0.121 (95% CI: 0.051-0.19; P < 0.001), respectively.

Conclusion: The random forest model effectively predicted the survival outcomes of patients with Ph-like ALL, which can aid clinicians to conduct personalized prognosis assessments in advance. Based on a web-based calculator, using random forest prediction models to calculate the prognosis of Ph-like ALL (https://songxiaodan03.shinyapps.io/RFpredictionmodelforPHlikeALL/) could facilitate healthcare professionals in carrying out clinical evaluation.