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August 4, 2025

Data from Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes

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Authors

DMDimitrios MathiosNNNoushin NiknafsAAAkshaya V. Annapragada

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Overview

Machine learning identifies brain cancer in 148 patients, indicating potential for early liquid biopsy diagnosis.

Key Points

  • MAIN FINDING: Machine learning methods effectively detect brain cancer using cfDNA fragmentome analysis.
  • KEY EVIDENCE: The method demonstrated an AUC of 0.90 in distinguishing gliomas from non-cancerous samples.
  • APPROACH: Analyzed cfDNA from 148 patients with brain cancer and 357 without, focusing on fragmentation profiles.
  • SIGNIFICANCE: This research advances noninvasive diagnostic techniques for brain cancer, enhancing early detection efforts.

Cite This Study

Mathios et al. (2025) studied this question.

synapsesocial.com/papers/689a0f86e6551bb0af8d0939https://doi.org/10.1158/2159-8290.c.7963861
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Also Consider

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

  1. 1Figure 1 from Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes2025 · 1 citations
  2. 2Cell-free DNA fragmentomics in cancer2025 · 52 citations
  3. 3Genome-wide cfDNA fragmentation patterns in cerebrospinal fluid reflect medulloblastoma groups2025 · 2 citations
  4. 4Detection of human brain cancers using genomic and immune cell characterization of cerebrospinal fluid through CSF-BAM2025 · 4 citations
  5. 5Figure 5 from Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes2025