Clinical significance of tumor deposits in gastric cancer after radical gastrectomy: a propensity score matching study

Objective The value of tumor deposits (TDs) in the prognosis and staging of gastric cancer (GC) is still under debate. This study aims to evaluate the prognostic value of TDs and the best ways to incorporate TDs in the TNM classification of GC. Methods Patients ( n = 3460) undergoing curative gastrectomy for GC in the West China Hospital from 2005 to 2017 were retrospectively reviewed and divided into two groups according to the TD status (positive vs. negative). Later, clinicopathological features and overall survival (OS) between the two groups were compared. Thereafter, the associations between the presence of TD and other clinicopathological factors were evaluated through logistic regression. In addition, univariate and multivariate Cox regression were conducted for determining prognostic factors. The possibility of selection bias was reduced through conducting the 1:1 propensity score matching (PSM) analysis. The modified classification systems proposed previously that incorporated TDs into the TNM staging system were assessed. Results There were 10.5% of patients (362/3460) diagnosed with TDs. TDs were significantly related to unfavorable factors such as advanced T stage and N stage and independently associated with poor prognosis. The 5-year OS of patients with TDs was significantly lower than that of patients without TDs (31.0% vs. 60.9%, P < 0.001), whereas higher than that of patients with peritoneal metastasis (31.0% vs. 5.0%, P < 0.001). In patients receiving chemotherapy, the 5-year OS of patients with TDs was also significantly lower than that of patients without TDs (42.0% vs. 50.9%, P = 0.026). Moreover, the system incorporating TDs in the TNM classification as metastatic lymph nodes outperformed others. Conclusions TDs are related to the aggressive characteristics and are an independent prognostic factor for GC. Incorporating TDs in the TNM classification as the metastatic lymph nodes increases the accuracy in predicting prognosis.