A scoring model for radiologic diagnosis of gastric leiomyomas (GLMs) with contrast-enhanced computed tomography (CE-CT): Differential diagnosis from gastrointestinal stromal tumors (GISTs)

• Published in: European journal of radiology 2021-01, Vol.134, p.109395-109395, Article 109395
• Main Authors: Xu, Jian-Xia, Ding, Qiao-Ling, Lu, Yuan-Fei, Fan, Shu-Feng, Rao, Qin-Pan, Yu, Ri-Sheng
• Format: Article
• Language: English
• Subjects: Contrast-enhanced CT; Diagnosis, Differential; Gastrointestinal stromal tumors; Gastrointestinal Stromal Tumors - diagnostic imaging; Humans; Leiomyoma; Leiomyoma - diagnostic imaging; Retrospective Studies; Stomach neoplasms; Stomach Neoplasms - diagnostic imaging; Tomography, X-Ray Computed
• ISSN: 0720-048X; 1872-7727
• DOI: 10.1016/j.ejrad.2020.109395

•Five characteristic CT features were independent risk factors for GLMs diagnosis with distinguishing from GISTs.•Scoring system was a concise radiologic diagnosis model with a high diagnostic value for GLMs differentiating from GISTs.•The diagnostic probability of GLMs showed an increase trend as stage increase of the score. To investigate CT findings and develop a diagnostic score model to differentiate GLMs from GISTs. This retrospective study included 109 patients with pathologically confirmed GLMs (n = 46) and GISTs (n = 63) from January 2013 to August 2018 who received CE-CT before surgery. Demographic and radiological features was collected, including lesion location, contour, presence or absence of intralesional necrosis and ulceration, growth pattern, whether the tumor involved EGJ, the long diameter (LD) /the short diameter (SD) ratio, pattern and degree of lesion enhancement. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors and establish a predictive model. Independent predictors for GLMs were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the model. Overall score distribution was divided into four groups to show differentiating probability of GLMs from GISTs. Five CT features were the independent predictors for GLMs diagnosis in multivariate logistic regression analysis, including esophagogastric junction (EGJ) involvement (OR, 367.9; 95 % CI, 5.8–23302.8; P =  0.005), absence of necrosis (OR, 11.9; 95 % CI, 1.0−138. 1; P =  0.048) and ulceration (OR, 151.9; 95 % CI, 1.4–16899.6; P =  0.037), degree of enhancement (OR, 9.3; 95 % CI, 3.2–27.4; P