Abstract
BACKGROUND: To retrospectively analyze the risk factors of liver metastases in patients with gastric cancer in a single center, and to establish a Nomogram prediction model to predict the occurrence of liver metastases. METHODS: A total of 96 patients with gastric cancer who were also diagnosed with liver metastasis (GCLM) and treated in our center from January 1, 2010 to December 31, 2020 were included. The clinical data of 1095 patients with gastric cancer who were diagnosed without liver metastases (GC) in our hospital from January 1, 2014 to December 31, 2017 were retrospectively compared by univariate and multivariate logistic regression. 309 patients diagnosed with gastric cancer in another medical center from January 1, 2014 to December 31, 2018 were introduced as external validation cohorts. RESULTS: Based on the training cohort, multivariate analysis revealed that tumor site (OR = 0.55, P = 0.046), N stage (OR = 4.95, P = 0.004), gender (OR = 0.04, P = 0.001), OPNI (OR = 0.95, P = 0.041), CEA (OR = 1.01, P = 0.018), CA724 (OR = 1.01, P = 0.006), CA242 (OR = 1.01, P = 0.006), WBC (OR = 1.13, P = 0.024), Hb (OR = 0.98, P < 0.001) were independent risk factors for liver metastasis in patients with gastric cancer, and Nomogram was established based on this analysis (C-statistics = 0.911, 95%CI 0.880-0.958), and the C-statistics of the external validation cohorts achieved 0.926. ROC analysis and decision curve analysis (DCA) revealed that the nomogram provided superior diagnostic value than single variety. CONCLUSIONS: By innovatively introducing a new tumor location classification method, systemic inflammatory response indicators such as NLR and PLR, and nutritional index OPNI, the risk factors of gastric cancer liver metastasis were determined and a predictive Nomogram model was established, which can provide clinical prediction for patients with gastric cancer liver metastasis.