Artificial intelligence-based diagnosis of upper gastrointestinal subepithelial lesions on endoscopic ultrasonography images

Keiko Hirai, Takamichi Kuwahara, Kazuhiro Furukawa, Naomi Kakushima, Satoshi Furune, Hideko Yamamoto, Takahiro Marukawa, Hiromitsu Asai, Kenichi Matsui, Yoji Sasaki, Daisuke Sakai, Koji Yamada, Takahiro Nishikawa, Daijuro Hayashi, Tomohiko Obayashi, Takuma Komiyama, Eri Ishikawa, Tsunaki Sawada, Keiko Maeda, Takeshi YamamuraTakuya Ishikawa, Eizaburo Ohno, Masanao Nakamura, Hiroki Kawashima, Masatoshi Ishigami, Mitsuhiro Fujishiro

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

Background: Endoscopic ultrasonography (EUS) is useful for the differential diagnosis of subepithelial lesions (SELs); however, not all of them are easy to distinguish. Gastrointestinal stromal tumors (GISTs) are the commonest SELs, are considered potentially malignant, and differentiating them from benign SELs is important. Artificial intelligence (AI) using deep learning has developed remarkably in the medical field. This study aimed to investigate the efficacy of an AI system for classifying SELs on EUS images. Methods: EUS images of pathologically confirmed upper gastrointestinal SELs (GIST, leiomyoma, schwannoma, neuroendocrine tumor [NET], and ectopic pancreas) were collected from 12 hospitals. These images were divided into development and test datasets in the ratio of 4:1 using random sampling; the development dataset was divided into training and validation datasets. The same test dataset was diagnosed by two experts and two non-experts. Results: A total of 16,110 images were collected from 631 cases for the development and test datasets. The accuracy of the AI system for the five-category classification (GIST, leiomyoma, schwannoma, NET, and ectopic pancreas) was 86.1%, which was significantly higher than that of all endoscopists. The sensitivity, specificity, and accuracy of the AI system for differentiating GISTs from non-GISTs were 98.8%, 67.6%, and 89.3%, respectively. Its sensitivity and accuracy were significantly higher than those of all the endoscopists. Conclusion: The AI system, classifying SELs, showed higher diagnostic performance than that of the experts and may assist in improving the diagnosis of SELs in clinical practice.

Original languageEnglish
Pages (from-to)382-391
Number of pages10
JournalGastric Cancer
Volume25
Issue number2
DOIs
Publication statusPublished - 03-2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Oncology
  • Gastroenterology
  • Cancer Research

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