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ORIGINAL RESEARCH article

Front. Cell Dev. Biol.

Sec. Cancer Cell Biology

Volume 13 - 2025 | doi: 10.3389/fcell.2025.1650810

This article is part of the Research TopicArtificial Intelligence in Multi-omics: Advancing Tumor Metastasis Prediction and Mechanism AnalysisView all articles

Survival Prediction for Philadelphia Chromosome-like Acute Lymphoblastic Leukemia by Machine Learning Analysis: A Multicenter Cohort Study

Provisionally accepted
Xiao-Dan SongXiao-Dan Song1Lin DannaLin Danna2Yunyan HeYunyan He3*Lu-Hong XuLu-Hong Xu4Lihua YangLihua Yang2*Liying LiuLiying Liu5Chi Kong LiChi Kong Li6Xiao-Rong LaiXiao-Rong Lai2Ya-Ting ZhangYa-Ting Zhang4Wan Wu QingWan Wu Qing7Xiao-Li ZhangXiao-Li Zhang8Xiang LanXiang Lan9Xing-Jiang LongXing-Jiang Long10Beiyan WuBeiyan Wu11Qi-Wen ChenQi-Wen Chen12
  • 1Graduate School, Graduate School, Guangxi Medical University, Nanning, China
  • 2Zhujiang Hospital of Southern Medical University, Guangzhou, China
  • 3The First Affiliated Hospital of Guangxi Medical University, Nanning, China
  • 4Sun Yat-Sen Memorial Hospital, Guangzhou, China
  • 5Graduate School, Guangxi Medical University, Nanning, China
  • 6Department of Pediatrics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 7The Second Xiangya Hospital of Central South University, Changsha, China
  • 8Department of pediatrics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
  • 9Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
  • 10Department of Pediatrics，liuzhou people's hospital, Liuzhou, China
  • 11The First Affiliated Hospital of Shantou University Medical College, Shantou, China
  • 12Department of Pediatrics, The First Affiliated Hospital of Nanchang University, Nanchang, China

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Background: This study aimed to develop an efficient survival model for predicting the 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-year, 3-year, 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 year, 3 years, 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 year, 3 years, 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. Conclusions: The random forest model effectively predicted the survival outcomes of patients with Ph-like ALL, which aided clinicians in conducting 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 work.

Keywords: Philadelphia chromosome-like acute lymphoblastic leukemia, machine learning, random forest, Minimal Residual Disease, Survival Prediction

Received: 20 Jun 2025; Accepted: 04 Aug 2025.

Copyright: © 2025 Song, Danna, He, Xu, Yang, Liu, Li, Lai, Zhang, Qing, Zhang, Lan, Long, Wu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Yunyan He, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
Lihua Yang, Zhujiang Hospital of Southern Medical University, Guangzhou, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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