BMC gastroenterology · 2026
CT semantic features of primary gastric cancer: a preoperative predictive model for peritoneal metastasis with superior efficacy to conventional direct CT assessment.
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Chen Shuxiang, Zhang Huijuan, Chen Yifan, Chen Shuo
Аннотация
OBJECTIVES: This study aims to evaluate the utility of CT semantic features of primary Gastric Cancer(GC) compared to conventional CT assessments for predicting Peritoneal Metastasis(PM) , and to construct a preoperative predictive model.
METHODS: We conducted a retrospective analysis involving 257 pathologically confirmed GC patients (92 with PM and 165 without PM), utilizing preoperative contrast-enhanced abdominal CT alongside clinicopathological data. Univariate and multivariate logistic regression analyses were performed to identify risk factors for PM, leading to the development of three predictive models: one based on primary tumor CT signs, another on peritoneal CT signs, and a combined model integrating both sets of features.
RESULTS: Independent PM predictors included the primary tumor's maximum size, serosal invasion, thickness, enhancement, and the presence of ascites. The primary tumor model demonstrated superior performance (AUC=0.920) compared to the peritoneal model (AUC=0.822, p<0.001). No significant difference was observed between the primary tumor model and the combined model (AUC=0.936, p=0.178). The combined model exhibited the highest sensitivity at 75.0%, while all models maintained a specificity of 98.2%.
CONCLUSIONS: CT semantic features of primary GC and ascites are effective in predicting PM. The primary tumor-based model surpasses conventional CT in performance, and the combined model further enhances sensitivity. This methodology improves preoperative PM assessment, may help reduce the incidence of non-therapeutic surgeries, and contributes positively to the prognosis of GC patients.
METHODS: We conducted a retrospective analysis involving 257 pathologically confirmed GC patients (92 with PM and 165 without PM), utilizing preoperative contrast-enhanced abdominal CT alongside clinicopathological data. Univariate and multivariate logistic regression analyses were performed to identify risk factors for PM, leading to the development of three predictive models: one based on primary tumor CT signs, another on peritoneal CT signs, and a combined model integrating both sets of features.
RESULTS: Independent PM predictors included the primary tumor's maximum size, serosal invasion, thickness, enhancement, and the presence of ascites. The primary tumor model demonstrated superior performance (AUC=0.920) compared to the peritoneal model (AUC=0.822, p<0.001). No significant difference was observed between the primary tumor model and the combined model (AUC=0.936, p=0.178). The combined model exhibited the highest sensitivity at 75.0%, while all models maintained a specificity of 98.2%.
CONCLUSIONS: CT semantic features of primary GC and ascites are effective in predicting PM. The primary tumor-based model surpasses conventional CT in performance, and the combined model further enhances sensitivity. This methodology improves preoperative PM assessment, may help reduce the incidence of non-therapeutic surgeries, and contributes positively to the prognosis of GC patients.