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December 8, 2025BloodOpen Access

AI-derived prediction of response and relapse to venetoclax plus hypomethylating agent based therapy in Acute Myeloid Leukemia

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Authors

SGSarvarinder GillMAMaher AlbitarJKJamie Koprivnikar

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Overview

Retrospective analysis shows AI predicts response to venetoclax therapy in 128 AML patients, suggesting improved relapse forecasting with transcriptomic data.

Key Points

  • Machine learning accurately predicts treatment response with 92.3% sensitivity and 75.7% specificity for venetoclax therapy.
  • The AI model achieved an Area Under the Curve of 0.876, demonstrating robust predictive performance.
  • Transcriptomic profiling provided superior accuracy for relapse prediction, outperforming clinical and genomic models.
  • Integrating AI-derived models may enable personalized treatment strategies, including potential early allogeneic transplant.

Cite This Study

Gill et al. (2025) studied this question.

synapsesocial.com/papers/69362f6e4fa91c937236e0adhttps://doi.org/10.1182/blood-2025-1731
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Real-world efficacy and prognostic modeling of venetoclax-enhanced intensive chemotherapy in fit AML2025
  2. 2Machine learning using bayesian networks to predict response in patients with newly diagnosed Acute Myeloid Leukemia2025
  3. 3Genetic risk model to predict outcomes for AML treated with HMA and venetoclax2025
  4. 4IDH1 mutation predicts response and survival in treatment-naïve Acute Myeloid Leukemia patients receiving with venetoclax with a hypomethylating agent2025
  5. 5A rapid gene expression profiler predicts tumor responsiveness and patient outcome for standard-of-care therapies in acute myeloid leukemia2025 · 1 citations