Novel CT based clinical nomogram comparable to radiomics model for identification of occult peritoneal metastasis in advanced gastric cancer

Abstract

BackgroundOccult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients remains a major diagnostic challenge. The aim of this study was to develop novel predictive models for identification of OPM in AGCs. MethodA total of 810 patients with primary AGCs from two hospitals were retrospectively selected and divided into training (n = 393), internal validation (n = 215) and external validation cohorts (n = 202). CT based machine learning models were built and tested to predict the OPM status in AGCs., which are 1) Radiomic signatures: using venous CT imaging features, 2) Clinical models: integrating tumor location, differentiation and extent of serosal exposure, and 3) Radiomics models: combining of radiomic signature, tumor location and tumor differentiation. ResultTotal incidence of OPM was 8.27% (67/810). Clinical models yielded comparable classification accuracy with the corresponding radiomics models with similar AUCs (0.902–0.969 vs. 0.896–0.975) while the radiomic signatures showed relatively low AUCs of 0.863–0.976. In the case where the specificity is higher than 90%, the overall sensitivity of clinical model and radiomics model for OPM positive cases was 76.1% (51/67) and 82.1% (55/67). A nomogram based on the logistic clinical model was drawn to facilitate the usage and verification of the clinical model. ConclusionBoth the novel CT based clinical nomogram and radiomics model provide promising method to yield high accuracy in identification of OPM in AGC patients.