ArticleThe oncologist2025
Prediction of methotrexate neurotoxicity using clinical, sociodemographic, and area-based information in children with acute lymphoblastic leukemia.
Article in The oncologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Phenotype-Specific Pharmacogenetic Patterns of Methotrexate Neurotoxicity in Pediatric Acute Lymphoblastic Leukemia.Journal of clinical medicine · 2026Article
- Digital Twin models to address long-term treatment toxicities in children and young adults with cancer.NPJ digital medicine · 2026Review
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Authors and funding
25 authors.
Funding
Abstract
backgroundMethotrexate is a critical component of pediatric acute lymphoblastic leukemia (ALL) therapy that can result in neurotoxicity which has been associated with an increased risk of relapse. We leveraged machine learning to develop a neurotoxicity risk prediction model in a diverse cohort of children with ALL.
methodsWe included children (age 2-20 years) diagnosed with ALL (2005-2019) and treated in Texas without pre-existing neurologic disease. Clinical information was obtained by medical record review. Neurotoxicity occurring post-induction and prior to maintenance therapy was defined as neurologic episodes occurring within 21 days of methotrexate. Suspected cases were independently confirmed by 2 pediatric oncologists. Demographic and clinical factors were compared using logistic regression. The dataset was randomly split (80/20) for training and testing. random forest (RF) with boosting and downsampling using 5-repeat, 10-fold cross-validation was used to construct a predictive model.
resultsNeurotoxicity developed in 115 (8.7%) of 1325 eligible patients. Several factors including older age at diagnosis (OR = 1.19, 95% CI: 1.15-1.24) and Latino ethnicity (OR = 2.79, 95% CI: 1.83-4.35) were associated with neurotoxicity. The RF had an area under the curve of 0.77 with a train error rate of 0.29 and a test error rate of 0.24. The overall sensitivity was 0.73, and specificity was 0.69.
conclusionsIn one of the largest studies of its kind, we developed a novel risk prediction model of methotrexate-related neurotoxicity. Ultimately, a validated model may help guide the development of personalized treatment strategies to reduce the burden of neurotoxicity in children diagnosed with ALL.
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