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Objectives <p>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.</p> Methods <p>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.</p> Results <p>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&lt;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%.</p> Conclusions <p>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.</p>

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CT semantic features of primary gastric cancer: a preoperative predictive model for peritoneal metastasis with superior efficacy to conventional direct CT assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Shuxiang Chen,
  • Huijuan Zhang,
  • Yifan Chen,
  • Shuo Chen

摘要

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.