Combination of the ratio between metastatic and harvested lymph nodes and negative lymph node count as a prognostic indicator in advanced gastric cancer: a retrospective cohort study

转移淋巴结与切除淋巴结比例及阴性淋巴结计数联合作为晚期胃癌预后指标:一项回顾性队列研究

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Abstract

BACKGROUND: The aim of our study was to examine the impact of the combination of the ratio between metastatic and harvested lymph nodes (RML) and negative lymph node (NLN) count on overall survival (OS) in patients with advanced gastric cancer (GC). METHODS: The clinicopathological data of 2,952 advanced GC patients who received curative resection between 1994 and 2015 were collected. They were divided into four groups according to the RML: 0, 0-0.1, 0.1-0.4, and >0.4. We distinguished survival differences through Kaplan-Meier analysis among the subgroups to investigate the impacts of the RML on OS in advanced GC patients. OS was examined according to clinicopathological variables. Spearman's correlation coefficient was used to assess the relationships between the RML and metastatic lymph node (MLN) count and NLN count. RESULTS: A total of 1,182 patients were enrolled into the study. The median follow-up time was 39 months (interquartile range 20 to 68 months). The 5-year OS rate of all 1,182 GC patients was 54.4%. Kaplan-Meier survival analysis showed that the median OS declined significantly with increasing RML (5-year survival rate 81.2% vs. 69.1% vs. 42.8% vs. 13.1%, P<0.001). As the NLN count increased, the survival rate of GC patients increased (5-year survival rate 12.8% vs. 25.2% vs. 60.2%, P<0.05). The RML, not NLN count, was identified as an independent factor for OS (P<0.001) through multivariate analysis. Spearman correlation analysis suggested that the RML was positively correlated with the number of MLNs (ρ=0.973, P<0.001) and inversely associated with the NLN count (ρ=-0.513, P<0.001). CONCLUSIONS: The RML is an independent prognostic predictor of OS in advanced GC patients, and the NLN count may serve as a supplementary strategy for the present tumor-node-metastasis (TNM) classification to further improve the prognostic prediction efficiency. The combination of the RML and NLN count should be an important predictor for current clinical applications.

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