{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T03:12:31Z","timestamp":1793243551512,"version":"4.5.0"},"reference-count":24,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"1","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>PREPARE-ALL was developed using data from the ICiCLe ALL-14 pretrial cohort across five centers, incorporating 33 clinical and laboratory features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>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%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-25-00222","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T21:00:16Z","timestamp":1769029216000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":4,"title":["PREPARE ALL: An Artificial Intelligence Tool for Predicting Relapse in Children With Acute Lymphoblastic 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