Machine learning-guided risk stratification in elderly AML based on genomic, immunophenotypic and therapeutic profiles.

Authors

Ling Zhang, Jiang Liu, Jingjing Liang, Xialin Zhang, Jialong Xin, Mingxuan Wei, Lina Wang, Jiaxin Huo, Chunxia Dong, Yuan Li, Yan Qiang, Junyan Zhang, Ruijuan Zhang

Published in

BMC geriatrics. Jun 22, 2026. Epub Jun 22, 2026.

Abstract

Elderly patients with acute myeloid leukemia (AML) exhibit considerable biological and clinical heterogeneity, hindering precise prognosis. Existing prognostic systems inadequately capture the complexity of elderly AML due to their reliance on data from younger cohorts and omission of key factors like immunophenotypic markers and therapeutic profiles. This study aimed to develop and internally validate a machine learning-based prognostic model specifically tailored to elderly AML patients.
A total of 156 patients were analyzed using a two-stage modeling strategy. Clinical and genomic variables were modeled first, followed by independent analysis of immunophenotypic features. Feature selection was performed using multilayer perceptron (MLP) and random forest (RF), while multivariate Cox regression was used for final model construction. Internal validation was conducted using 1000 bootstrap iterations to assess model stability and performance.
The model demonstrated strong predictive performance, with a concordance index (C-index) of 0.702. Time-dependent area under the curve (AUC) and calibration plots confirmed accurate prediction of 1-, 3-, and 5-year overall survival. Decision curve analysis indicated favorable net benefit across a range of threshold probabilities. Key independent prognostic factors identified included TP53 mutations, high CD13 expression, and IDH2 mutations.
This model provides a robust and interpretable tool for individualized risk stratification in elderly AML. By integrating genomic, immunophenotypic, and therapeutic variables, it may help optimize treatment decisions and improve outcomes for this vulnerable population. Future efforts should focus on external validation and integration of dynamic biomarkers.

PMID:
42324513
Bibliographic data and abstract were imported from PubMed on 22 Jun 2026.

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