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April 3, 2026Journal of Breast CancerOpen Access

Artificial Intelligence-Based Exosome Analysis for Improving Diagnostic Performance of Breast Lesions on Ultrasound: Protocol of a Prospective, Multicenter Cohort Study

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Authors

SSSung Eun SongHSHyunku ShinYPYong Park

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Overview

Multicenter cohort study investigates AI-enhanced exosome analysis for better breast lesion diagnosis, suggesting improved accuracy.

Key Points

  • To evaluate the effectiveness of AI-based exosome analysis in diagnosing breast lesions using ultrasound.
  • Prospective multicenter design
  • Included various clinical centers
  • Analysis of exosomes in breast lesions via ultrasound imaging
  • Assessing diagnostic performance metrics
  • Expected improvements in diagnostic accuracy
  • Potential reduction in false positives or negatives
  • Enhanced clarity in distinguishing benign from malignant lesions

Cite This Study

Song et al. (2026) studied this question.

synapsesocial.com/papers/69cf5de95a333a821460be76https://doi.org/10.4048/jbc.2025.0206
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Also Consider

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

  1. 1Artificial Intelligence-Enabled Exosomes in Precision Oncology: A Framework for Clinical Utility and Biomedical Applications2026
  2. 2The value of artificial intelligence in ultrasound imaging for predicting molecular subtypes of breast cancer: a meta-analysis2026 · 2 citations
  3. 3Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study2024 · 6 citations
  4. 4Ultrasound-based artificial intelligence for breast lesion classification2026 · 1 citations
  5. 5Validation of a deep learning-based AI system for HER2-targeted breast cancer assessment using ultrasound imaging in a clinical setting.2025