Value of markers of systemic inflammation for the prediction of postoperative progression in patients with pancreatic neuroendocrine tumors.

Abstract

Non-invasive prognostic predictors for rare pancreatic neuroendocrine tumors (PNETs) are lacking. We aimed to approach the prognostic value of preoperative systemic inflammatory markers in patients with PNETs. The clinical data of 174 patients with PNETs undergoing surgical treatment were retrospectively analyzed to explore the correlation of neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and platelet to white blood cell ratio (PWR) with clinicopathological parameters and the progression of tumor after the operation. The optimal cutoff values for predictors and the area under the curve (AUC) of the receiver operating characteristic (ROC) were estimated. Univariate and multivariate Cox proportional hazards models were used to assess the relation between NLR, LMR, PLR, and progression-free survival (PFS), examined by the Kaplan-Meier and log-rank tests. The scores of the NLR (P = 0.039) and PLR (P = 0.011) in the progression group were significantly higher than those in the progression-free group, and the LMR was significantly lower than those in the progression-free group (P = 0.001). The best cutoff values of NLR, LMR, and PLR before operation were 2.28, 4.36, and 120.91. The proportions of tumor progression in the high NLR group (P = 0.007) and high PLR group (P = 0.013) obviously increased, and the proportion of tumor development in the low LMR group was higher than that in the high LMR group (P < 0.001). The K-M survival curve showed that the progression-free survival rate was lower in the high NLR group (P = 0.004), the low LMR group (P < 0.001), and the high PLR group (P = 0.018). The results of the multivariate Cox proportional hazards model suggested that preoperative LMR (HR = 3.128, 95% CI: 1.107~8.836, P = 0.031) was an independent predictor of PFS. The markers of systemic inflammation, especially LMR, can predict the postoperative progression of PNETs.