ArticleJCO clinical cancer informatics2025
Development of a Dynamic Counterfactual Risk Stratification Strategy for Newly Diagnosed Patients With AML Treated With Venetoclax and Azacitidine.
Article in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- A comparative analysis in a clinical cohort: multiple imputation by chained equations and a novel super learner-based imputation approach.BMC medical research methodology · 2026Article
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Authors and funding
19 authors.
Funding
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Abstract
purposeThe objective of this study was to develop a flexible risk stratification strategy for AML that is specific for venetoclax plus azacitidine (ven/aza), addresses real-world data (RWD) issues, and is also adaptable to different use cases.
methodsA series of tunable risk models (RMs) were generated from a dynamic counterfactual machine learning (ML) strategy. These used a range of features from diagnostic AML samples and were tested using objective metrics on a single-institution cohort of 316 newly diagnosed patients treated with ven/aza. RM performance was tested using various model assumptions, data elements, and end points and with applications to an external AML real-world cohort (RWC).
resultsFavorable, intermediate, and adverse risk groups were identified in a series of ML-based RMs using different assumptions, for genetic-only or genetic-plus-phenotypic data elements and with overall survival and event-free survival as end points. Most RMs demonstrated equitable patient distribution (approximately 20%-40% in each risk group), significant separation between risk strata (log-rank-based
conclusionThe proposed ML strategy addresses a variety of RWD considerations and is readily tunable through coding and parameter updates for different contexts and use case needs. This strategy represents a novel approach to developing more effective RMs for AML and possibly other diseases.
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