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July 23, 2025

A Proteomics-Driven Machine Learning Tool for Distinguishing ET from pre-PMF

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Authors

LZLei ZhangQWQing WenTSTing Sun

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Overview

Retrospective study identifies a proteomic model that improves diagnosis of ET compared to clinical methods, highlighting its potential in MPN detection.

Key Points

  • The study compares diagnostic accuracy between proteomic profiling and clinical variables for essential thrombocythemia.
  • A 9-protein classifier achieved an AUC of 0.895, indicating robust discrimination between ET and pre-PMF.
  • Logistic regression was utilized to assess clinical predictors in a cohort of 440 patients for model development.
  • The proteomic approach shows promise for early-stage diagnosis of myeloproliferative neoplasms, enhancing clinical decision-making.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/689a0621e6551bb0af8cdc0bhttps://doi.org/10.21203/rs.3.rs-7128322/v1
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Also Consider

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

  1. 1Proteomic analysis identifying biomarkers in the progression from essential thrombocythemia to post-essential thrombocythemia myelofibrosis: A retrospective cohort study2025
  2. 2Characterisation of the megakaryocyte proteome in patients with Philadelphia negative myeloproliferative neoplasms2025
  3. 3A large language model-based framework for automated phenotypic characterization in myeloproliferative neoplasms2025
  4. 4Peripheral blood-based single-cell and genomic profiling of hematopoietic stem and progenitor cells enables accurate diagnosis and risk stratification in myeloproliferative neoplasms2025
  5. 5Determining sensitivity to FLT3 inhibitors prior to therapy in FLT3 mutant acute myelogenous leukemia2025