An Artificial Intelligence System for Distinguishing Between Gastrointestinal Stromal Tumors and Leiomyomas Using Endoscopic Ultrasonography (with video)

Abstract

Background Gastrointestinal stromal tumors (GISTs) and gastrointestinal leiomyomas (GILs) are the most common subepithelial lesions (SELs). All GISTs have malignant potential; however, GILs are considered benign. Current imaging cannot effectively distinguish GISTs from GILs. We aimed to develop an artificial intelligence (AI) system to differentiate these tumors using endoscopic ultrasonography (EUS). Methods The AI system was based on EUS images of patients with histologically-confirmed GISTs or GILs. Participants from four centers were collected to develop and retrospectively evaluate an AI-based system. The system was used when endosonographers considered SELs as GISTs or GILs. We used the system in a multicenter prospective diagnostic test to clinically explore whether joint diagnoses by endosonographers and the AI system can distinguish between GISTs and GILs to improve the total diagnostic accuracy of SELs. Results The AI system was developed using 10439 EUS images from 752 participants with GISTs or GILs. In the prospective test, 132 participants were histologically-diagnosed (36 GISTs, 44 GILs, and 52 other types of SELs) among 508 consecutive subjects. Through joint diagnoses, the total accuracy of endosonographers in diagnosing the 132 histologically-confirmed participants increased from 69.7% (95% confidence interval [CI]: 61.4–76.9%) to 78.8% (95% CI: 71–84.9%; p =0.012). The accuracy of endosonographers in diagnosing the 80 participants with GISTs or GILs increased from 73.8% (95% CI: 63.1–82.2%) to 88.8% (95% CI: 79.8–94.2%; p =0.012). Conclusions We developed an AI-based EUS diagnostic system that can effectively distinguish GISTs from GILs and improve the diagnostic accuracy of SELs.