Superficial spreading type of early gastric cancer with diagnostic difficulty and positive surgical margin: a case report.

An 83-year-old man visited our hospital because of difficulty swallowing. Gastroscopy revealed multiple ulcers and a reddish depression in the lesser curvature of the middle stomach. The initial biopsy showed regenerative atypia, so a gastroscopy was repeated every 3 months thereafter because of suspected malignancy. A biopsy performed 12 months after the initial gastroscopy revealed a well-differentiated adenocarcinoma. After determination of the planned oral resection line by two negative biopsies, laparoscopic distal gastrectomy was performed. The resected specimen showed a 0 - IIa + IIc lesion composed of well-to-moderately differentiated tubular adenocarcinoma, including hand-shaking-type gastric cancer. The oral resection margin was positive due to widespread mucosal extension; therefore, an additional total gastrectomy was needed. Cases of well-differentiated adenocarcinoma and its superficial extension may be difficult to diagnose via endoscopy and biopsy.

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

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.