基于人工智能的内镜检查与 miR148a 甲基化联合用于胃不确定异型增生的诊断。
Combination of artificial intelligence-based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis.
机构信息
Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Marianna University School of Medicine, Kanagawa, Japan.
Department of Internal Medicine, Kawasaki Rinko General Hospital, Kanagawa, Japan.
出版信息
J Clin Lab Anal. 2022 Jan;36(1):e24122. doi: 10.1002/jcla.24122. Epub 2021 Nov 22.
BACKGROUND AND AIM
Gastrointestinal endoscopy and biopsy-based pathological findings are needed to diagnose early gastric cancer. However, the information of biopsy specimen is limited because of the topical procedure; therefore, pathology doctors sometimes diagnose as gastric indefinite for dysplasia (GIN).
METHODS
We compared the accuracy of physician-performed endoscopy (trainee, n = 3; specialists, n = 3), artificial intelligence (AI)-based endoscopy, and/or molecular markers (DNA methylation: BARHL2, MINT31, TET1, miR-148a, miR-124a-3, NKX6-1; mutations: TP53; and microsatellite instability) in diagnosing GIN lesions. We enrolled 24,388 patients who underwent endoscopy, and 71 patients were diagnosed with GIN lesions. Thirty-two cases of endoscopic submucosal dissection (ESD) in 71 GIN lesions and 32 endoscopically resected tissues were assessed by endoscopists, AI, and molecular markers to identify benign or malignant lesions.
RESULTS
The board-certified endoscopic physicians group showed the highest accuracy in the receiver operative characteristic curve (area under the curve [AUC]: 0.931), followed by a combination of AI and miR148a DNA methylation (AUC: 0.825), and finally trainee endoscopists (AUC: 0.588).
CONCLUSION
AI with miR148s DNA methylation-based diagnosis is a potential modality for diagnosing GIN.
背景与目的
诊断早期胃癌需要进行胃肠内镜检查和基于活检的病理发现。然而，由于是局部操作，活检标本的信息有限；因此，病理医生有时会将发育不良诊断为不确定的胃内病变（GIN）。
方法
我们比较了医生进行的内镜检查（实习生，n=3；专家，n=3）、基于人工智能的内镜检查和/或分子标志物（DNA 甲基化：BARHL2、MINT31、TET1、miR-148a、miR-124a-3、NKX6-1；突变：TP53；和微卫星不稳定性）在诊断 GIN 病变中的准确性。我们招募了 24388 名接受内镜检查的患者，其中 71 名患者被诊断为 GIN 病变。在 71 例 GIN 病变的 32 例内镜黏膜下剥离术（ESD）和 32 例内镜切除组织中，由内镜医生、人工智能和分子标志物评估良性或恶性病变。
结果
经董事会认证的内镜医生组在受试者工作特征曲线（曲线下面积 [AUC]：0.931）中显示出最高的准确性，其次是人工智能和 miR148a DNA 甲基化的组合（AUC：0.825），最后是实习生内镜医生（AUC：0.588）。
结论
基于 miR148s DNA 甲基化的人工智能诊断是诊断 GIN 的一种潜在模式。
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