Superiority of the Ratio Between Negative and Positive Lymph Nodes for Predicting the Prognosis for Patients With Gastric Cancer View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-04

AUTHORS

Jingyu Deng, Rupeng Zhang, Liangliang Wu, Li Zhang, Xuejun Wang, Yong Liu, Xishan Hao, Han Liang

ABSTRACT

BACKGROUND: This study aimed to elucidate the prognostic prediction superiority of the ratio between negative and positive lymph nodes (RNP) in gastric cancer (GC). METHODS: The clinicopathologic data of 1,563 GC patients were analyzed to demonstrate the prognostic significances of the RNP stage. The tumor RNP metastasis (TRNPM) classification system also was evaluated to determine the potential superiorities of the prognostic prediction for GC patients. RESULTS: In the univariate survival analysis, both RNP stage and TRNPM classification were demonstrated to be relative factors in the overall survival (OS) of GC patients. Like the tumor-node-metastasis (TNM) and positive and dissected lymph node (TRPDM) classifications, the TRNPM classification was identified as an independently prognostic predictor of GC patients using multivariate survival analysis. However, TRNPM classification has smaller Akaike information criterion and Bayesian information criterion values than the TNM and TRPDM classifications, and TRNPM classification was demonstrated to be the most intensive indicator for the OS of GC patients using the case-control matched approach, which represented the comparative superiorities of prognostic prediction of TRNPM classification. CONCLUSION: The RNP stage should be considered as the optimal variable for evaluating the prognosis of GC in the clinic. More... »

PAGES

1258-1266

Identifiers

URI

http://scigraph.springernature.com/pub.10.1245/s10434-014-4121-8

DOI

http://dx.doi.org/10.1245/s10434-014-4121-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1000224155

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25319573


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RDF/XML is a standard XML format for linked data.

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