Machine learning to predict high-dose methotrexate-related neutropenia and fever in children with B-cell acute lymphoblastic leukemia

Abstract

Methotrexate (MTX), an antimetabolite for the treatment of leukemia, could cause neutropenia and subsequently fever, which might lead to treatment delay and affect prognosis. Here, we aimed to predict neutropenia and fever related to high-dose MTX using artificial intelligence. This study included 139 pediatric patients newly diagnosed with standard- or intermediate risk B-cell acute lymphoblastic leukemia. Fifty-seven SNPs of 16 genes were genotyped. Univariate and multivariate analysis were used to select SNPs and clinical covariates for model developing. Five machine learning algorithms combined with four resampling techniques were used to build optimal predictive model. The combination of random forest with adaptive synthetic appeared to be the best model for neutropenia (sensitivity = 0.935, specificity = 0.920, AUC = 0.927) and performed best for fever (sensitivity = 0.818, specificity = 0.924, AUC = 0.870). By machine learning, we have developed and validated comprehensive models to predict the risk of neutropenia and fever. Such models may be helpful for medical oncologists in quick decision-making.

Keywords:

• Machine learning
• acute lymphoblastic leukemia
• methotrexate
• neutropenia
• fever
• pharmacogenomics

Author contributions

M.Z. reviewed medical charts, collected data, and drafted the manuscript; Z.-b. C. contributed to the study design, and revised the manuscript; C.-c. D. performed machine learning model development; Q. Q. designed the study and revised the manuscript; G.-Q. W. processed data; S.-x. L. reviewed medical reports; F.-q. W. conceived, designed, and coordinated the study. All authors discussed and approved the final manuscript.

Disclosure statement

The authors declare that they have no competing interests.

Data availability statement

The data that support the findings of this study are openly available.

Additional information

Funding

This research was funded by the National Natural Science Foundation of China [No. 81503166, 81603208] and the Natural Scientific Foundation of Hunan Province in China [2018JJ3846].