Journal
MEDICAL SCIENCE MONITOR
Volume 23, Issue -, Pages 5574-5588Publisher
INT SCIENTIFIC INFORMATION, INC
DOI: 10.12659/MSM.907182
Keywords
Multivariate Analysis; Neutrophils; Pancreatic Neoplasms
Categories
Funding
- General Research Projects of the Department of Education of Zhejiang Province [Y201737812]
- National Natural Science Foundation of China [81401235]
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Background: The aim of this study was to evaluate the predictive and prognostic value of the preoperative neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in pancreatic neuroendocrine tumor (PNET) patients undergoing potentially curative resection. Material/Methods: A retrospective review of 172 patients with PNETs was conducted. Kaplan-Meier curves and multivariate Cox proportional models were used to calculate overall survival (OS) and disease-free survival (DFS). The predictive performance of the NLR was compared with other inflammation-based scores and conventional stratification systems using receiver operating characteristic (ROC) curve analysis. Results: Elevated NLR and PLR were both associated with advanced AJCC stage and high grade. In the univariate analysis, elevated NLR and PLR were both significantly associated with decreased OS and DFS. In the multivariate analysis, the preoperative NLR, but not the PLR, was an independent risk factor for OS (HR=4.471, 95% CI 1.531-13.054, p=0.006) and DFS (HR=2.531, 95% CI 1.202-5.329, p=0.015). The discriminatory capability of the NLR was superior to that of other inflammation-based scores in OS prediction. Furthermore, the predictive range was expanded by incorporating the NLR into the conventional stratification systems, including the AJCC stage and WHO classification systems. Conclusions: As an independent prognostic factor, an elevated preoperative NLR is superior to the PLR with respect to predicting clinical outcomes in PNET patients undergoing potentially curative resection. The incorporation of the NLR into the existing conventional stratification systems improved the predictive accuracy.
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