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

Hernández-Sánchez, Alberto, Martínez Elicegui, Javier, Sträng, Eric, Benner, Axel, Sobas, Marta and Thomas, Ian 2026. Machine learning provides individualized prediction of outcomes after first complete remission without allo‐HSCT consolidation in adult acute myeloid leukemia—A HARMONY study. HemaSphere 10 (9) , e70461. 10.1002/hem3.70461

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Medicine Research Institutes & Centres > Centre for Trials Research (CNTRR)
Additional Information:	Full list of authors available: https://doi.org/10.1002/hem3.70461
Publisher:	Wiley
ISSN:	2572-9241
Date of First Compliant Deposit:	8 September 2026
Date of Acceptance:	11 July 2026
Last Modified:	08 Sep 2026 09:30
URI:	https://orca.cardiff.ac.uk/id/eprint/189458

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