An artificial intelligence system for distinguishing between gastrointestinal stromal tumors and leiomyomas using endoscopic ultrasonography
An artificial intelligence system for distinguishing between gastrointestinal stromal tumors and leiomyomas using endoscopic ultrasonography
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利用超声内镜区分胃肠道间质瘤和平滑肌瘤的人工智能系统
DOI：
10.1055/a-1476-8931
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发表时间：
2021-04-07
期刊：
影响因子：
9.3
通讯作者：
Li, Xiaoyu
中科院分区：
文献类型：
--
作者：
Yang, Xintian;Wang, Han;Li, Xiaoyu
胃肠道间质瘤（gist）和胃肠道平滑肌瘤（GILs）是最常见的上皮下病变（sel）。所有胃肠道间质瘤均有恶性潜能；然而，女孩被认为是良性的。目前的影像学不能有效区分gist和gil。我们旨在开发一种人工智能（AI）系统，通过内窥镜超声检查（EUS）来区分这些肿瘤。方法人工智能系统基于组织学证实的gist或GILs患者的EUS图像。来自四个中心的参与者被收集来开发和回顾性评估基于人工智能的系统。当超声医师认为SELs是gist或GILs时，使用该系统。然后将其用于多中心前瞻性诊断试验，以临床探讨内镜和人工智能系统联合诊断是否可以区分gist和GILs，以提高SELs的总诊断准确性。结果人工智能系统的开发使用了752名gist或GILs患者的10 439张EUS图像。在前瞻性测试中，132名参与者在508名连续受试者中进行了组织学诊断（36名gist， 44名GILs和52名其他类型的sel）。通过联合诊断，132例组织学确诊患者的超声诊断总准确率从69.7%（95%可信区间[CI] 61.4% - 76.9%）提高到78.8% （95% CI 71.0% - 84.9%；P= 0.01）。超声诊断80例胃肠道间质瘤或胃肠道间质瘤的准确率从73.8% （95% CI 63.1% - 82.2%）提高到88.8% （95% CI 79.8% - 94.2%；P= 0.01）。结论基于人工智能的EUS诊断系统能够有效区分胃肠道间质瘤和胃肠道间质瘤，提高SELs的诊断准确率。
BackgroundGastrointestinal stromal tumors (GISTs) and gastrointestinal leiomyomas (GILs) are the most common subepithelial lesions (SELs). All GISTs have malignant potential; however, GILs are considered benign. Current imaging cannot effectively distinguish GISTs from GILs. We aimed to develop an artificial intelligence (AI) system to differentiate these tumors using endoscopic ultrasonography (EUS).MethodsThe AI system was based on EUS images of patients with histologically confirmed GISTs or GILs. Participants from four centers were collected to develop and retrospectively evaluate the AI-based system. The system was used when endosonographers considered SELs to be GISTs or GILs. It was then used in a multicenter prospective diagnostic test to clinically explore whether joint diagnoses by endosonographers and the AI system can distinguish between GISTs and GILs to improve the total diagnostic accuracy for SELs.ResultsThe AI system was developed using 10 439 EUS images from 752 participants with GISTs or GILs. In the prospective test, 132 participants were histologically diagnosed (36 GISTs, 44 GILs, and 52 other types of SELs) among 508 consecutive subjects. Through joint diagnoses, the total accuracy of endosonographers in diagnosing the 132 histologically confirmed participants increased from 69.7 % (95 % confidence interval [CI] 61.4 %–76.9 %) to 78.8 % (95 %CI 71.0 %–84.9 %;P= 0.01). The accuracy of endosonographers in diagnosing the 80 participants with GISTs or GILs increased from 73.8 % (95 %CI 63.1 %–82.2 %) to 88.8 % (95 %CI 79.8 %–94.2 %;P= 0.01).ConclusionsWe developed an AI-based EUS diagnostic system that can effectively distinguish GISTs from GILs and improve the diagnostic accuracy of SELs.