The oncologist · 2025
Prediction of methotrexate neurotoxicity using clinical, sociodemographic, and area-based information in children with acute lymphoblastic leukemia.
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Harris Rachel D, Taylor Olga A, Gramatges Maria Monica, Hughes Amy E, Zobeck Mark, Pruitt Sandi, Bernhardt M Brooke, Chavana Ashley, Huynh Van, Ludwig Kathleen, Klesse Laura, Heym Kenneth, Griffin Timothy, Erana Rodrigo, Bernini Juan Carlos, Choi Ashley, Ohno Yuu, Richard Melissa A, Morrison Alanna C, Chen Han, Yu Bing, Lupo Philip J, Rabin Karen R, Scheurer Michael E, Brown Austin L
Аннотация
BACKGROUND: Methotrexate 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.
METHODS: We 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.
RESULTS: Neurotoxicity 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.
CONCLUSIONS: In 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.
METHODS: We 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.
RESULTS: Neurotoxicity 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.
CONCLUSIONS: In 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.