Construction of a prognostic hierarchical model of intermediate-risk acute myeloid leukemia based on machine learning

To refine prognostic stratification for intermediate-risk acute myeloid leukemia (IR-AML) by leveraging machine learning to integrate clinical and genomic features and relate them to survival outcomes.

We conducted a two-cohort study comprising a single-center development cohort from Beijing Tsinghua Changgung Hospital (n = 56) and an independent external cohort from The Cancer Genome Atlas (TCGA; n = 79). Demographics and mutational profiles were analyzed alongside survival outcomes. We developed three tree-based models-random forests, gradient-boosted decision trees (GBDT), and XGBoost-on the Tsinghua Changgung cohort, using stratified five-fold cross-validation for internal validation.

In internal cross-validation, tree-based learners showed strong discrimination (best GBDT AUROC 0.98, 95% confidence interval (CI) 0.91-1.00). On the external TCGA cohort, GBDT achieved AUROC 0.73 (95% CI 0.62-0.83). Model-agnostic explanations (Shapley additive explanations) consistently highlighted white blood cell count, age, transplantation, and TET2 among top contributors.

An interpretable machine learning framework built from accessible clinical and genomic variables provided quantitative risk discrimination for IR-AML across development and external test cohorts, supporting individualized risk assessment and informing refinement of prognostic stratification.