Gastric subepithelial tumor: long-term natural history and risk factors for progression

Kang, Sam-Sik; Lee, Eunwoo; Choi, Jinju; Chung, Hwa‐Jin; Cho, Soo‐Jeong; Kim, Sang Gyun

doi:10.1007/s00464-021-08901-4

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Our findings are similar to those of previous studies. Kim et al 6 found that the prevalence of gastric SETs in healthy participants was 1.51%, and no increase in size was detected in 93% of gastric SETs. A single-center study of 948 patients demonstrated that 8.5% of gastric SETs showed significant change at a median follow-up of 24 months.…”

Section: Discussionmentioning

confidence: 98%

“…The natural course of gastric SETs is still unclear; thus, the appropriate management strategy is controversial. 12 Kim et al 6 proposed biennial endoscopic follow-up for small SETs <1 cm, annual follow-up for SETs 1 to 2 cm, and close follow-up every 6 months or less for large SETs or SETs with malignant potential. ESGE recommends surveillance of gastric SETs without definite diagnosis, with endoscopy at 3 to 6 months, then 2 to 3 years intervals for lesions <1 cm and 1 to 2 years intervals for lesions 1 to 2 cm in size.…”

Section: Discussionmentioning

confidence: 99%

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Prevalence and natural course of incidental gastric subepithelial tumors

Heo,

Yang,

Song

et al. 2024

Clin Endosc

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The prevalence of gastric SETs increases with age. Most gastric SETs do not progress during long-term endoscopic examinations, and the risk of an increase in size is low in asymptomatic small SETs without irregular borders.Background/Aims: Gastric subepithelial tumors (SETs) are often encountered during the upper gastrointestinal endoscopic screening.We assessed the prevalence of gastric SETs and the risk factors for their progression. Methods: We reviewed the electronic medical records of 30,754 patients who underwent upper gastrointestinal endoscopic screening at our medical center between January 2013 and December 2016.Results: Among the 30,754 patients examined, 599 (1.9%) had gastric SETs. The prevalence increased with age and was 9.56% in patients aged ≥70 years. In total, 262 patients underwent serial endoscopy for more than 6 months. The median age was 68 years (interquartile range [IQR], 61-74), and the number of females was 167 (63.7%). During a median follow-up of 58 months (IQR, 38-75), 22 patients (8.4%) showed significant changes in tumor size. An irregular border (odds ratio, 4.623; 95% confidence interval, 1.093-19.558; p=0.037) was a significant risk factor for progression. Seven patients underwent surgical or endoscopic resections. The pathologies of gastric SETs included leiomyomas (n=3), gastrointestinal stromal tumors (n=2), and lipomas (n=2). Conclusions:The prevalence of gastric SETs increases with age. Most gastric SETs do not progress during long-term endoscopic examinations, and the risk of an increase in size is low in asymptomatic small SETs without irregular borders.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Prevalence and natural course of incidental gastric subepithelial tumors

Heo,

Yang,

Song

et al. 2024

Clin Endosc

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“…Moreover, the situations of gastric SELs are more complicated. 23 Indeed, most of the gastric SELs originate from the muscularis propria, 24 so we chose this category to build the prototype of the model. Our subsequent work may include all SELs from the esophagus, duodenum, and large intestine, with all layers to build a modified model.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in the Prediction of Gastrointestinal Stromal Tumors on Endoscopic Ultrasonography Images: Development, Validation and Comparison with Endosonographers

Lü

et al. 2023

Gut and Liver

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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.

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Natural history of gastric leiomyoma

Park,

Ahn,

et al. 2024

Surg Endosc

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Gastric subepithelial tumor: long-term natural history and risk factors for progression

Cited by 5 publications

References 28 publications

Prevalence and natural course of incidental gastric subepithelial tumors

Prevalence and natural course of incidental gastric subepithelial tumors

Artificial Intelligence in the Prediction of Gastrointestinal Stromal Tumors on Endoscopic Ultrasonography Images: Development, Validation and Comparison with Endosonographers

Natural history of gastric leiomyoma

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