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


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).


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.


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).


AI via a deep learning algorithm using EUS images could predict the malignant potential of gastric GISTs with high accuracy.

Graphic Abstract

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Armed Forces Institute of Pathology


Artificial intelligence


Artificial neural network


Convolutional neural network


Endoscopic ultrasonography


Fine needle aspiration


Gastrointestinal stromal tumor


High-power field


Negative predictive value


Positive predictive value


Receiver Operating Characteristic


Standard deviation


Synthetic minority oversampling technique


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Correspondence to Hakan Senturk.

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Seven, G., Silahtaroglu, G., Kochan, K. et al. Use of Artificial Intelligence in the Prediction of Malignant Potential of Gastric Gastrointestinal Stromal Tumors. Dig Dis Sci (2021).

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  • Artificial intelligence
  • Gastric gastrointestinal stromal tumors
  • Mitotic index
  • Risk classification