Multi-slice CT features predict pathological risk classification in gastric stromal tumors larger than 2 cm: a retrospective multicenter study

Abstract

Abstract Background Accurate risk stratification for gastric stromal tumors (GSTs) has become increasingly important. The Armed Forces Institute of Pathology (AFIP) had higher accuracy and reliability in prognostic assessment and treatment strategies for patients with GSTs. This study aimed to investigate the feasibility of multi-slice CT (MSCT) features of GSTs in predicting AFIP risk classification. Methods Clinical data and MSCT features of 424 patients with solitary GSTs were retrospectively reviewed. According to pathological AFIP risk criteria, 424 GSTs were divided into low-risk group (n = 282), moderate-risk group (n = 72) and high-risk group (n = 70). Clinical data and MSCT features of GSTs were compared among the three groups. Results We found significant differences in tumor location, morphology, necrosis, ulceration, growth pattern, feeding artery, vascular-like enhancement, fat positive sign around GSTs, CT value in venous phase, CT value increment in venous phase, longest diameter, and maximum short diameter (p < 0.05). Two nomogram model were successfully constructed to predict the risk of GSTs. Low- vs high-risk group, the independent risk factors of high-risk GSTs included location, ulceration, longest diameter; The area under the receiver operating characteristic curve (AUC) of prediction model was 0.911 (95% CI: 0.872–0.951 ), the sensitivity and the specificity were 80.0% and 89.0%, respectively. Moderate- vs high-risk group, morphology, necrosis and feeding artery were independent risk factors of high-risk of GSTs, with an AUC value of 0.826 (95% CI: 0.759–0.893), the sensitivity and the specificity were 85.7% and 70.8%, respectively. Conclusion MSCT features of GSTs and nomogram model have great practical value in predicting pathological AFIP risk classification between high risk and non-high risk groups before surgery. There is limitations for differentiating the low- and moderate-risk groups.