Development and Validation of a CT Radiomics and Clinical Feature Model to Predict Omental Metastases for Locally Advanced Gastric Cancer

Objectives: We employed radiomics and clinical features to develop and validate a preoperative prediction modelto estimate the metastases status of locally advanced gastric cancer (LAGC). Materials: and methods A total of 356 patients (training cohort, n = 250; test cohort, n = 106) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the extracted radiomics features, and a radiomics score model was constructed. Finally, a prediction model of omental metastases status and a nomogram were constructed combining the radiomics scores and selected clinical features. An area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to validate the capability of the prediction model and nomogram in the training cohort. Calibration curves and decision curve analysis (DCA) were used to evaluate the prediction model and nomogram. The prediction model was internally validated by the test cohort. In addition, 104 patients from another hospital's clinical and imaging data were gathered for external validation. Results: In the training cohort, the combined prediction (CP) model (AUC=0.871, 95% CI 0.798-0.945) of the radiomics scores combined with the clinical features, compared with clinical features prediction (CFP) model (AUC=0.795, 95% CI 0.710-0.879) and radiomics scores prediction (RSP) model (AUC=0.805, 95% CI 0.730-0.879), had the better predictive ability. The Hosmer-Lemeshow test of the CP model showed that the prediction model did not deviate from the perfect fitting ( p =0.893). In the DCA, the clinical net benefit of the CP model was higher than that of the CFP model and RSP model. In the test and validation cohorts, the AUC values of the CP model were 0.836 (95%CI 0.726-0.945) and 0.779 (95%CI 0.634-0.923), respectively. Conclusions: The preoperative APCT-based clinical-radiomics nomogram showed good performance in predicting omental metastases status in LAGC, which may contribute to clinical decision-making.