Artificial Intelligence in the Prediction of Gastrointestinal Stromal Tumors on Endoscopic Ultrasonography Images: Development, Validation and Comparison with Endosonographers
Yi Lu,
Jiachuan Wu,
Minhui Hu,
Qinghua Zhong,
Limian Er,
Huihui Shi,
Weihui Cheng,
Ke Chen,
Yuan Liu,
Bingfeng Qiu,
Qiancheng Xu,
Guangshun Lai,
Yufeng Wang,
Yuxuan Luo,
Jinbao Mu,
Wenjie Zhang,
Min Zhi,
Jiachen Sun
Affiliations
Yi Lu
Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
Jiachuan Wu
Digestive Endoscopy Center, Guangdong Second Provincial General Hospital, Guangzhou, China
Minhui Hu
Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
Qinghua Zhong
Department of Endoscopic Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
Limian Er
Department of Endoscopy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
Huihui Shi
Department of Endoscopy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
Weihui Cheng
Department of Gastroenterology, Yangjiang Hospital of Traditional Chinese Medicine, Yangjiang, China
Ke Chen
Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai, China
Yuan Liu
Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai, China
Bingfeng Qiu
Department of Gastroenterology, Zhoushan Hospital of Zhejiang Province, Zhoushan, China
Qiancheng Xu
Department of Gastroenterology, Zhoushan Hospital of Zhejiang Province, Zhoushan, China
Guangshun Lai
Department of Gastroenterology, Lianjiang People’s Hospital, Lianjiang, China
Yufeng Wang
Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China
Yuxuan Luo
Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China
Jinbao Mu
Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China
Wenjie Zhang
Tianjin Center for Medical Devices Evaluation and Inspection, Tianjin, China
Min Zhi
Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
Jiachen Sun
Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions: We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.
