Machine learning provides individualized prediction of outcomes after first complete remission without allo-HSCT consolidation in adult acute myeloid leukemia-A HARMONY study.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative treatment option for a significant proportion of patients with acute myeloid leukemia (AML), and it is generally recommended when the relapse risk without allo-HSCT outweighs the estimated non-relapse mortality significantly. While current recommendations for allo-HSCT are based on risk groups, there is considerable heterogeneity within these individual categories. We analyzed 2550 intensively treated AML patients aged 18-70 with cytogenetic and next-generation sequencing data from the HARMONY Alliance database, who did not receive allo-HSCT in first complete remission (CR1). A non-parametric machine learning (ML) model based on Bayesian Additive Regression Trees (BART) integrated clinical variables and genomic aberrations to provide individualized outcome estimations. External validation was performed in a cohort of 714 patients enrolled in UK-NCRI trials. The predictive performance of the HARMONY ML model, measured by the area under the time-dependent receiver operating curve (AUC(t)), was superior to European LeukemiaNet (ELN)2022 risk classification in estimating 5-year overall survival (0.741 vs. 0.700), 5-year relapse-free survival (0.752 vs. 0.705), and 5-year cumulative incidence of relapse (0.742 vs. 0.708), which was confirmed in the external validation cohort. Notably, the model revealed substantial heterogeneity within ELN2022 risk groups, identifying a significant proportion of favorable-risk patients with a predicted 5-year CIR > 40%, who could potentially benefit from allo-HSCT in CR1. The HARMONY ML model provides individualized risk prediction in intensively treated adult AML patients and supports more tailored therapeutic decisions regarding allo-HSCT in CR1, which should be further advanced by integrating measurable residual disease in the future.