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December 8, 2025Blood

Which molecular patterns of high-risk myeloid malignancies are concordantly identified using two machine learning algorithms?

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Authors

TKTariq KewanWBWaled BahajAKAhmad Kiwan

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Overview

Machine learning algorithms reveal distinct molecular clusters in higher-risk myelodysplastic syndromes and acute myeloid leukemia, suggesting improved prognostic scoring systems.

Key Points

  • Machine learning algorithms identified distinct molecular clusters that improve prognosis in myeloid malignancies.
  • Overall survival was significantly longer in high-risk myelodysplastic syndrome patients compared to acute myeloid leukemia patients.
  • Molecular clusters based on latent-factor modeling were compared to prognostic scoring systems for clinical implications.
  • Findings highlight the potential of molecular classification for disease understanding beyond traditional pathology.

Cite This Study

Kewan et al. (2025) studied this question.

synapsesocial.com/papers/69362f574fa91c937236d9f5https://doi.org/10.1182/blood-2025-3485
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Also Consider

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

  1. 1Molecular subclusters across the continuum of myelodysplastic neoplasms and acute myeloid leukaemia define distinct clinical entities2025
  2. 2Deciphering clonal progression in MDS via longitudinal sequencing2025
  3. 3Machine learning uncovers invariant evolutionary molecular trajectories in MDS.2025
  4. 4CMML2AML: Machine-learning discovery of co-mutations predictive of blast transformation in chronic myelomonocytic leukemia2025
  5. 5Redefining the myeloid disease continuum: Machine learning–based development and validation of an integrated multimodal classification of myeloid neoplasms2025 · 1 citations