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December 8, 2025BloodOpen Access

Non-invasive classification & molecular subtyping of mature lymphoid neoplasms by cell-free DNA profiling

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Authors

JMJurik MutterBSBrian J. SworderBTBenoît Tessoulin

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Overview

Observational analysis predicts tumor subtypes in mature lymphoid neoplasms via non-invasive cell-free DNA techniques, indicating improved diagnostic accuracy.

Key Points

  • High accuracy in predicting tumor subtypes using cell-free DNA from plasma, enhancing non-invasive diagnostics.
  • Machine learning model achieves 94% overall accuracy for classifying diverse mature lymphoid neoplasms.
  • Validations performed across 10 types including DLBCL, HL, and others with a significant focus on molecular subtyping.
  • Highlights potential for improved clinical impact and reduced procedural risks associated with tissue biopsies.

Cite This Study

Mutter et al. (2025) studied this question.

synapsesocial.com/papers/69362f5a4fa91c937236daf7https://doi.org/10.1182/blood-2025-5330
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