在基于人工智能的EUS弹性图形下,胃肠道 stromal 瘤的风险分层系统
Chenxia Zhang1, Wei Tan1, Xun Li1
1Department of Gastroenterology, Renmin Hospital of Wuhan University; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
Endoscopic ultrasound
|January 26, 2026
概括
一个基于人工智能的系统准确地识别了胃肠道 stromal 瘤 (GISTs),并使用内镜超声波弹性图 (EUS-E) 将其恶性潜力分层. 这一目标工具通过评估组织硬性和改进风险评估,有助于临床管理.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 胃肠道 stromal 瘤 (GISTs) 由于其恶性潜力,需要进行手术前风险分层.
- 内镜超声波弹性图 (EUS-E) 评估组织度,但目前的方法是主观的.
- 需要一个客观的工具来准确地使用EUS-E.使用GIST风险分层.
研究的目的:
- 开发和评估基于人工智能的系统,用于客观地识别GIST和风险分层.
- 使用EUS-E.E.来区分GIST与其他粘膜下瘤 (SMTs).
- 客观地评估GISTs的恶性潜力.
主要方法:
- 110个GIST案件和189个SMT案件的回顾性收集,这些案件正在接受EUS-E.
- 使用2625个EUS B模式图像构建AI分类和细分模型.
- 从弹性图像中提取弹性值 (EUS-E-AI),用于风险评估.
主要成果:
- 人工智能分类和细分模型实现了高精度 (95.8%) 和子系数 (0.967).
- 在低风险 (0.268) 和高风险 (0.186) GIST恶性瘤组之间,EUS-E-AI值显著不同 (P < 0.001).
- 0.224的EUS-E-AI截止值准确差异化了风险组,准确率为92.6%.
结论:
- 一个基于人工智能的系统和弹性指标可以准确客观地识别GISTs.
- 开发的系统有效地基于使用EUS-E的恶性潜力对GIST进行了分层.
- 这种人工智能工具有助于GIST管理的临床决策.
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