Development and validation of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumour markers

Abstract

Background: Peritoneal metastasis (PM) is not uncommon in patients with gastric cancer(GC), which affects the clinical treatment decisions, but the relevant examination measures are not efficient detection. Our goal is to develop a clinical radiomics nomogram to better predict peritoneal metastases. Methods: 3480 patients from 2 centers were divided into 1 training, 1 internal validation, and 1 external validation cohorts (1949 in the internal training set and 704 in the validation set, and 827 in the external validation cohorts) with clinicopathologically confirmed GC. We recruited 11 clinical factors including age, sex, smoke or not, tumor size, differentiation, Borrmann type, Location, T stage, serum tumor markers (STMs) comprising carbohydrate antigen 19 − 9 (CA19-9), carbohydrate antigen 72 − 4 (CA72-2), and carcinoembryonic antigen(CEA) to develop the radiomics nomogram. For clinical predictive feature selection and establish clinical models, the Statistical methods of analysis of variance (ANOVA), relief and recursive feature elimination (RFE) and logistic regression analysis were used. To develop combined predictive models, tumor diameters, type, tumor location, T stage and STMs were finally selected. The discriminative ability of the nomogram to predict PM is evaluated by the AUC, decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of the nomogram. Results: The AUC of clinical models was 0.762 in the training cohorts, 0.772 in the internal validation cohorts, and 0.758 in the external validation cohort. However, when combined with STMs, the AUC was improved to 0.806,0.839 and 0.801 respectively. DCA analysed that the combined nomogram was of good clinical evaluation value to predict PM in GC. Conclusions The present study proposed a clinical nomogram with a combination of clinical risk factors and radiomics features that can potentially be applied in the individualized preoperative prediction of PM in GC patients.