Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. Issue 1 (27th June 2020)

• Record Type: Journal Article
• Authors: Iwagami, Hiroyoshi; Ishihara, Ryu; Aoyama, Kazuharu; Fukuda, Hiromu; Shimamoto, Yusaku; Kono, Mitsuhiro; Nakahira, Hiroko; Matsuura, Noriko; Shichijo, Satoki; Kanesaka, Takashi; Kanzaki, Hiromitsu; Ishii, Tatsuya; Nakatani, Yasuki; Tada, Tomohiro
• Other Names: Ang Tiing–Leong guestEditor.
• Is Part Of: Journal of gastroenterology and hepatology. Volume 36:Issue 1(2021)
• Page Start: 131
• Page End: 136
• Publication Date: 2020-06-27
• Subjects: adenocarcinoma -- AI -- artificial intelligence -- detection -- EGJ -- esophageal; Gastroenterology -- Periodicals; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; Gastroenterology -- Periodicals; Liver Diseases -- Periodicals; 616.33
• DOI: 10.1111/jgh.15136
• Languages: English
• ISSNs: 0815-9319

Abstract

Abstract: Background and Aim: Conventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited because early lesions are asymptomatic, and the associated changes in the mucosa are subtle. There are no reports on artificial intelligence (AI) diagnosis for E/J cancer from Asian countries. Therefore, we aimed to develop a computerized image analysis system using deep learning for the detection of E/J cancers. Methods: A total of 1172 images from 166 pathologically proven superficial E/J cancer cases and 2271 images of normal mucosa in esophagogastric junctional from 219 cases were used as the training image data. A total of 232 images from 36 cancer cases and 43 non‐cancerous cases were used as the validation test data. The same validation test data were diagnosed by 15 board‐certified specialists (experts). Results: The sensitivity, specificity, and accuracy of the AI system were 94%, 42%, and 66%, respectively, and that of the experts were 88%, 43%, and 63%, respectively. The sensitivity of the AI system was favorable, while its specificity for non‐cancerous lesions was similar to that of the experts. Interobserver agreement among the experts for detecting superficial E/J was fair (Fleiss' kappa = 0.26, z = 20.4, P < 0.001). Conclusions: Our AI system achieved high sensitivity and acceptable specificity for the detection of E/J cancers and may be a good supporting tool for the screening of E/J cancers.