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Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis
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 dyspla...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761468/ https://www.ncbi.nlm.nih.gov/pubmed/34811809 http://dx.doi.org/10.1002/jcla.24122 |
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author | Watanabe, Yoshiyuki Oikawa, Ritsuko Agawa, Shuhei Matsuo, Yasumasa Oda, Ichiro Futagami, Seiji Yamamoto, Hiroyuki Tada, Tomohiro Itoh, Fumio |
author_facet | Watanabe, Yoshiyuki Oikawa, Ritsuko Agawa, Shuhei Matsuo, Yasumasa Oda, Ichiro Futagami, Seiji Yamamoto, Hiroyuki Tada, Tomohiro Itoh, Fumio |
author_sort | Watanabe, Yoshiyuki |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8761468 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87614682022-01-20 Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis Watanabe, Yoshiyuki Oikawa, Ritsuko Agawa, Shuhei Matsuo, Yasumasa Oda, Ichiro Futagami, Seiji Yamamoto, Hiroyuki Tada, Tomohiro Itoh, Fumio J Clin Lab Anal Research Articles 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. John Wiley and Sons Inc. 2021-11-22 /pmc/articles/PMC8761468/ /pubmed/34811809 http://dx.doi.org/10.1002/jcla.24122 Text en © 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Watanabe, Yoshiyuki Oikawa, Ritsuko Agawa, Shuhei Matsuo, Yasumasa Oda, Ichiro Futagami, Seiji Yamamoto, Hiroyuki Tada, Tomohiro Itoh, Fumio Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis |
title | Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis |
title_full | Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis |
title_fullStr | Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis |
title_full_unstemmed | Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis |
title_short | Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis |
title_sort | combination of artificial intelligence‐based endoscopy and mir148a methylation for gastric indefinite dysplasia diagnosis |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761468/ https://www.ncbi.nlm.nih.gov/pubmed/34811809 http://dx.doi.org/10.1002/jcla.24122 |
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