A nomogram combining inflammatory markers and clinical factors predicts survival in patients with diffuse glioma

Abstract

In this study, we aimed to investigate the prognostic value of neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), and platelet/lymphocyte ratio (PLR) in diffuse glioma, and to establish a prognostic nomogram accordingly.The hematologic and clinicopathological data of 162 patients with primary diffuse glioma who received surgical treatment from January 2012 to December 2018 were retrospectively analyzed. Receiver operator characteristic (ROC) curve was carried out to determine the optimal cut-off values for NLR, MLR, PLR, age, and Ki-67 index, respectively. Kaplan-Meier method was used to investigate the correlation between inflammatory indicators and prognosis of glioma patients. Univariate and multivariate Cox regression were performed to evaluate the independent prognostic value of each parameter in glioma. Then, a nomogram was developed to predict 1-, 3-, and 5-year postoperative survival in diffuse glioma patients based on independent prognostic factors. Subsequent time-dependent ROC curve, calibration curve, decision curve analysis (DCA), and concordance index (C-index) were performed to assess the predictive performance of the nomogram.The Kaplan-Meier curve indicated that patients with high levels of NLR, MLR, and PLR had a poor prognosis. In addition, we found that NLR level was associated with World Health Organization (WHO) grade and IDH status of glioma. The multivariate Cox analysis indicated that resection extent, WHO grade, and NLR level were independent prognostic factors, and we established a nomogram that included these three parameters. The evaluation of the nomogram indicated that the nomogram had a good predictive performance, and the addition of NLR could improve the accuracy.NLR, MLR, and PLR were prognostic factors of diffuse glioma. In addition, the nomogram including NLR was reliable for predicting survival of diffuse glioma patients.

Figure 1

ROC curves analysis for optimal cut-off values for (A) NLR, (B) MLR, and (C) PLR. The optimal cut-off values for NLR, MLR and PLR were 2.78, 0.235, and 134.4, respectively.

Figure 2

Relationship of NLR, MLR, and PLR with clinicopathologic features and prognosis of glioma. Correlation of NLR, MLR, and PLR with (A--C) WHO grade and (D--F) IDH status. (G--I) Kaplan--Meier curves were used to compare the OS of patients with high and low levels of NLR, MLR ,and PLR, respectively.

Figure 3

Construction and evaluation of the prognostic nomogram model. (A) Nomogram was established to predict OS of glioma patients. (B) The Kaplan--Meier curve shows difference in OS between the high-risk and low-risk groups. (C) Calibration curves were used to compare nomogram prediction and actual observation. (D--F) Time-dependent ROC curves were used to assess the accuracy of predicting 1-, 3-, and 5-year survival. (G--I) Decision curve analysis for evaluating the net clinical benefit.