ArticleJournal of breast cancer2026
Artificial Intelligence-Based Exosome Analysis for Improving Diagnostic Performance of Breast Lesions on Ultrasound: Protocol of a Prospective, Multicenter Cohort Study.
Article in Journal of breast cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06672302 (A Multi-center, Single Arm, Prospective Exploratory in Vitro Diagnostic Devices Clinical Performance Study to Evaluate of in Vitro Diagnostic Devices for Auxiliary Diagnosis That Classifies High-risk and Low Risk Patients With Breast Cancer by Analyzing Surface-enhanced Raman Spectroscopy), which is not on this map. Cited by 1 paper.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multi-center, Single Arm, Prospective Exploratory in Vitro Diagnostic Devices Clinical Performance Study to Evaluate of in Vitro Diagnostic Devices for Auxiliary Diagnosis That Classifies High-risk and Low Risk Patients With Breast Cancer by Analyzing Surface-enhanced Raman Spectroscopy (SERS) Profiles of Extracellular Vesicles (EVs) Extracted From Human Plasma Using Artificial Intelligence
Who cites it
1 citing paper in PubMed.
- Theranostic Smart Bionanosensors for Solid Tumors: Advances, Challenges, and Future Directions.Applied biochemistry and biotechnology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purposeExosome-surface enhanced Raman spectroscopy-artificial intelligence platform (exosome-SERS-AI) is an innovative liquid biopsy method that acquires SERS signals from plasma exosomes and analyzes them using deep learning models to diagnose cancer. This study aimed to evaluate whether exosome-SERS-AI could increase the diagnostic accuracy of ultrasonography (US) for suspicious breast lesions.
methodsThis prospective multicenter study enrolled 500 patients between November 2024 and December 2025. Eligible participants will be women aged ≥ 40 years who will undergo US performed by specialized breast radiologists and have suspicious breast lesions assigned to a Breast Imaging Reporting and Data System (BI-RADS) category 3-5 assessment. A 6 mL whole blood sample was collected from each participant. After plasma separation, SERS, which is highly sensitive to exosomes, was employed to measure Raman signals, and the acquired data were processed using artificial intelligence algorithms. Following sampling, all patients underwent US-guided core needle biopsy for breast lesions classified as BI-RADS category 4 and 5, and 12-months of follow-up US for lesions classified as BI-RADS category 3. Histopathological examination was used as the reference standard for BI-RADS 4 and 5 lesions, whereas stability on 12-month follow-up US was used as the reference standard for BI-RADS 3 lesions. The cohort is expected to have an equal distribution of benign and malignant cases. The following outcome measures were compared between US alone and the combination of exosome-SERS-AI with US: sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve. Enrollment is expected to be completed by 2025, and the study results are expected to be presented in 2026. DISCUSSION: This prospective multicenter study will evaluate the performance of exosome-SERS-AI compared to US in women with BI-RADS categories 3-5. Participant enrollment is ongoing.
trial registrationClinicalTrials.gov Identifier: NCT06672302. Registered on November 4, 2024.
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