Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors

Yosuke Minoda, Eikichi Ihara, Nao Fujimori, Shuzaburo Nagatomo, Mitsuru Esaki, Yoshitaka Hata, Xiaopeng Bai, Yoshimasa Tanaka, Haruei Ogino, Takatoshi Chinen, Qingjiang Hu, Eiji Oki, Hidetaka Yamamoto, Yoshihiro Ogawa

研究成果: ジャーナルへの寄稿学術誌査読

1 被引用数 (Scopus)

抄録

Gastrointestinal stromal tumors (GISTs) are common subepithelial lesions (SELs) and require treatment considering their malignant potential. We recently developed an endoscopic ultrasound-based artificial intelligence (EUS-AI) system to differentiate GISTs from non-GISTs in gastric SELs, which were used to train the system. We assessed whether the EUS-AI system designed for diagnosing gastric GISTs could be applied to non-gastric GISTs. Between January 2015 and January 2021, 52 patients with non-gastric SELs (esophagus, n = 15; duodenum, n = 26; colon, n = 11) were enrolled. The ability of EUS-AI to differentiate GISTs from non-GISTs in non-gastric SELs was examined. The accuracy, sensitivity, and specificity of EUS-AI for discriminating GISTs from non-GISTs in non-gastric SELs were 94.4%, 100%, and 86.1%, respectively, with an area under the curve of 0.98 based on the cutoff value set using the Youden index. In the subanalysis, the accuracy, sensitivity, and specificity of EUS-AI were highest in the esophagus (100%, 100%, 100%; duodenum, 96.2%, 100%, 0%; colon, 90.9%, 100%, 0%); the cutoff values were determined using the Youden index or the value determined using stomach cases. The diagnostic accuracy of EUS-AI increased as lesion size increased, regardless of lesion location. EUS-AI based on gastric SELs had good diagnostic ability for non-gastric GISTs.

本文言語英語
論文番号16640
ジャーナルScientific reports
12
1
DOI
出版ステータス出版済み - 12月 2022

!!!All Science Journal Classification (ASJC) codes

  • 一般

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