Abstract
This study aimed to investigate whether the computed tomography (CT) finding of irregular extensive ulceration (IEU) can serve as a predictor of liver metastasis (LIM) in patients with gastric gastrointestinal stromal tumours (GISTs). This study retrospectively collected clinical and imaging data from 286 patients diagnosed with low-, intermediate-, or high-risk gastric GISTs, or primary lesions with LIM from three medical institutions. The patients were categorised into non-LIM and LIM groups according to whether they had synchronous or metachronous LIM. Multivariate logistic regression analyses were performed to identify significant predictors of LIM. Additionally, receiver operating characteristic (ROC) curve, subgroup, and pathologic-radiologic correlation analyses were conducted. A total of 124 patients were ultimately enroled. There were significant differences in sex, site, growth pattern, size, shape, ulceration and Ki-67 expression between LIM and non-LIM groups. ROC curve analysis demonstrated that IEU had the highest area under the curve for predicting LIM (AUC = 0.842; 95% CI: 0.760-0.924; p < 0.001). Multivariate analysis indicated that IEU was the most significant independent predictor of high LIM risk (OR = 88.62; 95% CI: 2.80-2803.54; p = 0.011). Subgroup analysis showed that IEU was more frequently associated with male sex, age ≤ 55 years, proximal sites, irregular shapes, mixed growth patterns, and a high Ki-67 expression. The CT feature of IEU serves as an independent predictor of LIM in gastric GISTs and is strongly associated with high Ki-67 expression. Question Accurate assessment of LIM risk in patients with gastric GISTs is crucial, yet current non-invasive predictors remain inadequate. Findings IEU on CT is an independent predictor of LIM, with high diagnostic accuracy and a significant association with elevated Ki-67 expression. Clinical relevance IEU on CT scans enables non-invasive risk stratification for LIM in gastric GISTs. Our study refined the assessment of ulceration types, highlighting significant heterogeneity, which may guide personalised treatment strategies.
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