PREPARE ALL: An Artificial Intelligence Tool for Predicting Relapse in Children With Acute Lymphoblastic Leukemia.

PURPOSE The Pediatric Relapse Prediction and Risk Evaluation for Acute Lymphoblastic Leukemia (PREPARE-ALL) tool aims to predict relapse in pediatric ALL by integrating clinical expertise with artificial intelligence and machine learning (ML), particularly Extreme Gradient Boosting (XGBoost). PREPARE-ALL demonstrates that multicenter, protocol-driven clinical and laboratory data can be used through ML to generate reproducible relapse predictions with greater sensitivity than individual clinician assessments. METHODS PREPARE-ALL was developed using data from the ICiCLe ALL-14 pretrial cohort across five centers, incorporating 33 clinical and laboratory features. RESULTS Among 2,252 patients enrolled in the study, 565 (25.1%) relapsed. Using an 80:20 train-test split, XGBoost achieved a sensitivity of 68.5% (245/447 relapses detected). Additional metrics included a positive predictive value of 31.3%, a negative predictive value of 82.8%, an accuracy of 54.8%, and a specificity of 50.3%. Key predictors of relapse included high hyperdiploidy and BCR-ABL1 fusion positive, positive measurable residual disease status at the end of induction, sex, age, highest presenting WBC, and final risk group. Three clinicians scored the validation data set; the developed model achieved a higher recall (68.5%) compared with clinical judgment (approximately 31%-36%). CONCLUSION PREPARE-ALL identifies twice as many relapses as clinicians and serves as a practical decision-support tool for early relapse triage and treatment planning, enabling timely therapeutic adjustments and improved outcomes in pediatric ALL.

• Publication year

  2026

• Venue

  JCO Clinical Cancer Informatics

• Publication date

  2026-01-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1200/CCI-25-00222PMID 41564371
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery

  2025cited by this paper
• Risk stratified treatment for childhood acute lymphoblastic leukaemia: a multicentre observational study from India

  2025cited by this paper
• Prediction of lymph node metastasis in cervical cancer patients using AdaBoost machine learning model: analysis of risk factors.

  2025cited by this paper
• Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector

  2023cited by this paper
• A Review of Acute Lymphocytic Leukemia (ALL) in the Pediatric Population: Evaluating Current Trends and Changes in Guidelines in the Past Decade

  2023cited by this paper
• Predicting delayed methotrexate elimination in pediatric acute lymphoblastic leukemia patients: an innovative web-based machine learning tool developed through a multicenter, retrospective analysis

  2023cited by this paper
• A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection

  2022cited by this paper
• Protocol for ICiCLe-ALL-14 (InPOG-ALL-15-01): a prospective, risk stratified, randomised, multicentre, open label, controlled therapeutic trial for newly diagnosed childhood acute lymphoblastic leukaemia in India

  2022cited by this paper
• Key challenges for delivering clinical impact with artificial intelligence

  2019cited by this paper
• A Review of Challenges and Opportunities in Machine Learning for Health.

  2018cited by this paper
• Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

  2016cited by this paper
• Analysis of clinical prognostic variables for Chronic Lymphocytic Leukemia decision-making problems

  2016cited by this paper
• XGBoost: A Scalable Tree Boosting System

  2016cited by this paper
• Acute lymphoblastic leukemia: A single center experience with Berlin, Frankfurt, and Munster-95 protocol

  2015cited by this paper
• Acute Lymphoblastic Leukemia in Children.

  2015cited by this paper
• Receiver operating characteristic curve in diagnostic test assessment.

  2010cited by this paper
• Applications of Machine Learning in Cancer Prediction and Prognosis

  2006cited by this paper
• Predicting breast cancer survivability: a comparison of three data mining methods

  2005cited by this paper
• Applied Logistic Regression, Second Edition

  1989cited by this paper

• No citing papers are available for this paper.