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

IF 2.4 4区 医学 Q3 ONCOLOGY
Sung Eun Song, Hyunku Shin, Yong Park, Yeonho Choi, Seung Pil Jung
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引用次数: 0

Abstract

Purpose: Exosome-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.

Methods: This 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 registration: ClinicalTrials.gov Identifier: NCT06672302. Registered on November 4, 2024.

基于人工智能的外泌体分析提高乳腺病变超声诊断性能:一项前瞻性多中心队列研究方案。
目的:外泌体表面增强拉曼光谱-人工智能平台(exosome-SERS-AI)是一种创新的液体活检方法,它从血浆外泌体获取SERS信号,并利用深度学习模型对其进行分析,以诊断癌症。本研究旨在评估外泌体sers - ai是否可以提高超声对可疑乳腺病变的诊断准确性。方法:这项前瞻性多中心研究于2024年11月至2025年12月招募了500名患者。符合条件的参与者将是年龄≥40岁的女性,她们将接受由专业乳腺放射科医生进行的超声检查,并且有可疑的乳房病变,并被分配到乳腺成像报告和数据系统(BI-RADS)分类3-5的评估中。从每位参与者身上采集6毫升全血样本。血浆分离后,利用对外泌体高度敏感的SERS测量拉曼信号,并利用人工智能算法对采集到的数据进行处理。血样采集后,所有患者均行超声引导下的核心针活检检查BI-RADS 4类和5类乳腺病变,并对BI-RADS 3类病变进行12个月的超声随访。BI-RADS 4、5级病变以组织病理学检查为参考标准,BI-RADS 3级病变以12个月随访US稳定性为参考标准。预期入选队列的良性和恶性病例分布均匀。比较单独US与外泌体- sers - ai联合US的结果指标:敏感性、特异性、阳性预测值、阴性预测值和受试者工作特征曲线下面积。预计2025年完成招生,2026年公布研究结果。讨论:这项前瞻性多中心研究将评估外泌体- sers - ai在BI-RADS 3-5类女性中的表现,并与美国进行比较。参与者登记正在进行中。试验注册:ClinicalTrials.gov标识符:NCT06672302。于2024年11月4日注册。
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来源期刊
Journal of Breast Cancer
Journal of Breast Cancer 医学-肿瘤学
CiteScore
3.80
自引率
4.20%
发文量
43
审稿时长
6-12 weeks
期刊介绍: The Journal of Breast Cancer (abbreviated as ''J Breast Cancer'') is the official journal of the Korean Breast Cancer Society, which is issued quarterly in the last day of March, June, September, and December each year since 1998. All the contents of the Journal is available online at the official journal website (http://ejbc.kr) under open access policy. The journal aims to provide a forum for the academic communication between medical doctors, basic science researchers, and health care professionals to be interested in breast cancer. To get this aim, we publish original investigations, review articles, brief communications including case reports, editorial opinions on the topics of importance to breast cancer, and welcome new research findings and epidemiological studies, especially when they contain a regional data to grab the international reader''s interest. Although the journal is mainly dealing with the issues of breast cancer, rare cases among benign breast diseases or evidence-based scientifically written articles providing useful information for clinical practice can be published as well.
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