Predictive Factors for Differentiating Gastrointestinal Stromal Tumors from Leiomyomas Based on Endoscopic Ultrasonography Findings in Patients with Gastric Subepithelial Tumors: A Multicenter Retrospective Study
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Background/Aims: The utility of endoscopic ultrasonography (EUS) for differentiating gastrointestinal stromal tumors (GISTs)and leiomyomas of the stomach is not well known. We aimed to evaluate the ability of EUS for differentiating gastric GISTs andleiomyomas. Methods: We retrospectively reviewed the medical records of patients with histopathologically proven GISTs (n=274) andleiomyomas (n=87). In two consensus meetings, the inter-observer variability in the EUS image analysis was reduced. Using logisticregression analyses, we selected predictive factors and constructed a predictive model and nomogram for differentiating GISTs fromleiomyomas. A receiver operating characteristic (ROC) curve analysis was performed to measure the discrimination performance inthe development and internal validation sets. Results: Multivariate analysis identified heterogeneity (odds ratio [OR], 9.48), non-cardia (OR, 19.11), and older age (OR, 1.06)as independent predictors of GISTs. The areas under the ROC curve of the predictive model using age, sex, and four EUS factors(homogeneity, location, anechoic spaces, and dimpling or ulcer) were 0.916 (sensitivity, 0.908; specificity, 0.793) and 0.904 (sensitivity,0.908; specificity, 0.782) in the development and internal validation sets, respectively. Conclusions: The predictive model and nomogram using age, sex and homogeneity, tumor location, presence of anechoic spaces, andpresence of dimpling or ulcer on EUS may facilitate differentiation between GISTs and leiomyomas.
Abstract
Background/Aims: The utility of endoscopic ultrasonography (EUS) for differentiating gastrointestinal stromal tumors (GISTs)and leiomyomas of the stomach is not well known. We aimed to evaluate the ability of EUS for differentiating gastric GISTs andleiomyomas. Methods: We retrospectively reviewed the medical records of patients with histopathologically proven GISTs (n=274) andleiomyomas (n=87). In two consensus meetings, the inter-observer variability in the EUS image analysis was reduced. Using logisticregression analyses, we selected predictive factors and constructed a predictive model and nomogram for differentiating GISTs fromleiomyomas. A receiver operating characteristic (ROC) curve analysis was performed to measure the discrimination performance inthe development and internal validation sets. Results: Multivariate analysis identified heterogeneity (odds ratio [OR], 9.48), non-cardia (OR, 19.11), and older age (OR, 1.06)as independent predictors of GISTs. The areas under the ROC curve of the predictive model using age, sex, and four EUS factors(homogeneity, location, anechoic spaces, and dimpling or ulcer) were 0.916 (sensitivity, 0.908; specificity, 0.793) and 0.904 (sensitivity,0.908; specificity, 0.782) in the development and internal validation sets, respectively. Conclusions: The predictive model and nomogram using age, sex and homogeneity, tumor location, presence of anechoic spaces, andpresence of dimpling or ulcer on EUS may facilitate differentiation between GISTs and leiomyomas.
참고문헌 (16)
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