Development and validation of a machine learning model based on routine laboratory tests for predicting FLT3-ITD mutations in acute myeloid leukaemia.

Feline McDonough sarcoma (FMS)-like tyrosine kinase 3 internal tandem duplication (FLT3-ITD) mutations are clinically important in acute myeloid leukaemia (AML). We developed and validated a machine learning model using routine laboratory tests to estimate the probability of FLT3-ITD mutations in newly diagnosed AML to prioritise rapid molecular testing. Consecutive patients treated at Ruijin Hospital during 2021-2024 were included. Those diagnosed in 2021-2023 comprised the training cohort, and those in 2024 formed the internal temporal validation cohort, and the BeatAML2 cohort served as external validation. Following Boruta feature selection, seven machine learning algorithms were evaluated via area under the receiver operating characteristic curve (AUC). The optimal model was interpreted using SHapley Additive exPlanations (SHAP) and deployed as a web-based calculator. Overall, 514 patients were included (training, n = 254; validation, n = 84; external validation, n = 176). Boruta algorithm selected five candidate features, and a simple four-feature model was constructed. Gradient boosting machine (GBM) performed best, with AUCs of 0.847, 0.772 and 0.760 in the training, internal validation and external validation cohorts respectively. SHAP analysis showed absolute blast count contributed most to the model. The GBM model may help identify patients with newly diagnosed AML who should be prioritised for rapid molecular testing for FLT3-ITD mutations.