Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 4, 2025

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

View Full Paper
Ask AI
Bookmark
Share

Authors

DMDimitrios MathiosNNNoushin NiknafsAAAkshaya V. Annapragada

Discussion

Loading...

Member takes

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
View Full Paper
Ask AI
Bookmark
Share

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. 2Multidimensional cell-free DNA fragmentomics enables early detection of breast cancer2025 · 1 citations
  3. 3Multidimensional cell-free DNA fragmentomics enables early detection of breast cancer2025
  4. 4Cell-free DNA fragmentomes for noninvasive detection of liver cirrhosis and other diseases2026 · 4 citations
  5. 5Abstract 4950: Applying fragmentomics profiles of plasma cell-free DNA for breast cancer detection2024