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December 9, 2025Breast Cancer ResearchOpen Access

Multidimensional cell-free DNA fragmentomics enables early detection of breast cancer

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Authors

LYLi-xian YangMAMengyang AnHSHeng Song

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Overview

Analysis reveals a novel approach to improve sensitivity in breast cancer detection, highlighting the role of cfDNA and machine learning algorithms.

Key Points

  • The aim is to enhance early breast cancer detection using cell-free DNA fragmentomics and machine learning.
  • Involvement of 191 healthy participants and 204 breast cancer patients.
  • Plasma cfDNA samples profiled using whole-genome sequencing.
  • Assessment of various cfDNA characteristics and machine learning models in the training cohort.
  • Developed an ensemble model combining three cfDNA features with six machine learning algorithms.
  • Achieved 93.3% sensitivity and 94.6% specificity in the training cohort (AUC 0.983).
  • Attained 96.5% sensitivity and 93.7% specificity in validation cohort (AUC 0.989).

Cite This Study

Yang et al. (2025) studied this question.

synapsesocial.com/papers/69401efa2d562116f28f9a7ehttps://doi.org/10.1186/s13058-025-02190-8
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Also Consider

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

  1. 1Multidimensional cell-free DNA fragmentomics enables early detection of breast cancer2025 · 1 citations
  2. 2Multi-Dimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer2024 · 35 citations
  3. 3Abstract 4950: Applying fragmentomics profiles of plasma cell-free DNA for breast cancer detection2024
  4. 4Assessment of plasma cell-free DNA fragmentation for multi-cancer early detection: An independent clinical validation study.2024 · 2 citations
  5. 5Integrative fragmentomic and mutational signature profile of plasma cfDNA for early lung cancer detection2026