ArticleWorld journal of gastrointestinal oncology2023
Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors.
Article in World journal of gastrointestinal oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Imaging Strategies and Research Advances in Gastrointestinal Stromal Tumors.Current treatment options in oncology · 2026Review
- Intratumoral and Peritumoral Radiomics based on Contrast-enhanced CT Images: Biomarkers to Predict the Risk-Grade of Gastrointestinal Stromal TumorsCurrent medical imaging · 2026Article
- CT feature of irregular extensive ulceration as a predictor of liver metastasis in gastric gastrointestinal stromal tumours.European radiology · 2025Article
- Association between calcification and risk stratification in gastric gastrointestinal stromal tumors.Abdominal radiology (New York) · 2025Article
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9 authors.
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Abstract
backgroundComputed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs).
aimTo determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs.
methodsThe clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification.
resultsThere were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (
conclusionCT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.
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