Differentiating Gastrointestinal Stromal Tumors From Leiomyomas of Upper Digestive Tract Using Convolutional Neural Network Model by Endoscopic Ultrasonography

Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas: a multicenter diagnostic study

Background

Gastrointestinal stromal tumors (GISTs) represent the most prevalent type of subepithelial lesions (SELs) with malignant potential. Current imaging tools struggle to differentiate GISTs from leiomyomas. This study aimed to create and assess a real-time artificial intelligence (AI) system using endoscopic ultrasonography (EUS) images to differentiate between GISTs and leiomyomas.

Methods

The AI system underwent development and evaluation using EUS images from 5 endoscopic centers in China between January 2020 and August 2023. EUS images of 1101 participants with SELs were retrospectively collected for AI system development. A cohort of 241 participants with SELs was recruited for external AI system evaluation. Another cohort of 59 participants with SELs was prospectively enrolled to assess the real-time clinical application of the AI system. The AI system's performance was compared to that of endoscopists. This study is registered with Chictr.org.cn, Number ChiCT2000035787.

Findings

The AI system displayed an area under the curve (AUC) of 0.948 (95% CI: 0.921-0.969) for discriminating GISTs and leiomyomas. The AI system's accuracy (ACC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) reached 91.7% (95% CI 87.5%-94.6%), 90.3% (95% CI 83.4%-94.5%), 93.0% (95% CI 87.2%-96.3%), 91.9% (95% CI 85.3%-95.7%), and 91.5% (95% CI 85.5%-95.2%), respectively. Moreover, the AI system exhibited excellent performance in diagnosing ≤20 mm SELs (ACC 93.5%, 95% CI 0.900-0.969). In a prospective real-time clinical application trial, the AI system achieved an AUC of 0.865 (95% CI 0.764-0.966) and 0.864 (95% CI 0.762-0.966) for GISTs and leiomyomas diagnosis, respectively, markedly surpassing endoscopists [AUC 0.698 (95% CI 0.562-0.834) for GISTs and AUC 0.695 (95% CI 0.546-0.825) for leiomyomas].

Interpretation

We successfully developed a real-time AI-assisted EUS diagnostic system. The incorporation of the real-time AI system during EUS examinations can assist endoscopists in rapidly and accurately differentiating various types of SELs in clinical practice, facilitating improved diagnostic and therapeutic decision-making.

Funding

Science and Technology Commission Foundation of Shanghai Municipality, Science and Technology Commission Foundation of the Xuhui District, the Interdisciplinary Program of Shanghai Jiao Tong University and the Research Funds of Shanghai Sixth people's Hospital.