Open AccessCombination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis
Author(s) -
Watanabe Yoshiyuki,
Oikawa Ritsuko,
Agawa Shuhei,
Matsuo Yasumasa,
Oda Ichiro,
Futagami Seiji,
Yamamoto Hiroyuki,
Tada Tomohiro,
Itoh Fumio
Publication year - 2022
Publication title -
journal of clinical laboratory analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 50
eISSN - 1098-2825
pISSN - 0887-8013
DOI - 10.1002/jcla.24122
Subject(s) - endoscopy , medicine , biopsy , endoscopic submucosal dissection , dysplasia , microsatellite instability , gastroenterology , cancer , pathology , radiology , biology , gene , allele , biochemistry , microsatellite
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.