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

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.