Use of Artificial Intelligence in the Prediction of Malignant Potential of Gastric Gastrointestinal Stromal Tumors


Digestive Diseases and Sciences, vol.67, no.1, pp.273-281, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 67 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1007/s10620-021-06830-9
  • Journal Name: Digestive Diseases and Sciences
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, CINAHL, EMBASE, MEDLINE, Veterinary Science Database
  • Page Numbers: pp.273-281
  • Keywords: Artificial intelligence, Gastric gastrointestinal stromal tumors, Mitotic index, Risk classification
  • Istanbul Medipol University Affiliated: Yes


Background and Aims: This study aimed to investigate whether AI via a deep learning algorithm using endoscopic ultrasonography (EUS) images could predict the malignant potential of gastric gastrointestinal stromal tumors (GISTs). Methods: A series of patients who underwent EUS before surgical resection for gastric GISTs were included. A total of 685 images of GISTs from 55 retrospectively included patients were used as the training data set for the AI system. Convolutional neural networks were constructed to build a deep learning model. After applying the synthetic minority oversampling technique, 70% of the generated images were used for AI training and 30% were used to test AI diagnoses. Next, validation was performed using 153 EUS images of 15 patients with GISTs. In addition, conventional EUS features of 55 patients in the training cohort were evaluated to predict the malignant potential of GISTs and mitotic index. Results: The overall sensitivity, specificity, and accuracy of the AI system for predicting malignancy risk were 83%, 94%, and 82% in the training dataset, and 75%, 73%, and 66% in the validation cohort, respectively. When patients were divided into low-risk and high-risk groups, sensitivity, specificity, and accuracy increased to 99% in the training dataset and 99.7%, 99.7%, and 99.6%, respectively, in the validation cohort. No conventional EUS features were found to be associated with either malignant potential or mitotic index (P > 0.05). Conclusions: AI via a deep learning algorithm using EUS images could predict the malignant potential of gastric GISTs with high accuracy. Graphic Abstract: [Figure not available: see fulltext.]