Preoperative prediction of serosal invasion in gastric cancer using multiphase contrast-enhanced CT radiomics.

Objective: This study aimed to develop and validate a radiomics-based model using multiphase contrast-enhanced CT for the preoperative prediction of serosal invasion in gastric cancer, and to evaluate whether the integration of clinical and CT morphological features could further improve predictive performance. Methods: A retrospective analysis was conducted on 183 patients with surgically confirmed gastric cancer. All patients underwent multiphase CT (arterial, venous, and delayed phases). Radiomic features were extracted from volumetric regions of interest (VOIs). After feature selection and radiomics model construction, a combined nomogram was developed incorporating independent clinical and imaging predictors. Models were evaluated in training and validation cohorts using AUC, accuracy, sensitivity, and specificity. Results: The arterial plus delayed (AP+DP) phase radiomics model demonstrated the best performance, with AUCs of 0.8256 and 0.8179 in the training and validation sets, respectively. Multivariate analysis identified serum CA724 levels and serosal stranding as independent predictors. The combined nomogram incorporating these factors and the radiomics signature achieved AUCs of 0.8626 and 0.8641 in the training and validation sets, respectively. A clinical model was also established, with AUCs of 0.7738 and 0.7654 for the training and validation sets. Conclusion: A radiomics model based on AP+DP CT phases shows strong potential for predicting serosal invasion in GC.The integration of CA72-4 and serosal stranding further increased the AUC of the combined model. The combined model achieved a statistically significant improvement in AUC over the clinical model in the training set, providing a non-invasive tool to support preoperative decision making.