Glioma is the most common form of brain tumor and has high lethality. The authors of this study aimed to elucidate the efficiency of preoperative inflammatory markers, including neutrophil/lymphocyte ratio (NLR), derived NLR (dNLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), and prognostic nutritional index (PNI), and their paired combinations as tools for the preoperative diagnosis of glioma, with particular interest in its most aggressive form, glioblastoma (GBM).
The medical records of patients newly diagnosed with glioma, acoustic neuroma, meningioma, or nonlesional epilepsy at 3 hospitals between January 2011 and February 2016 were collected and retrospectively analyzed. The values of NLR, dNLR, PLR, LMR, and PNI were compared among patients suffering from glioma, acoustic neuroma, meningioma, and nonlesional epilepsy and healthy controls by using nonparametric tests. Correlations between NLR, dNLR, PLR, LMR, PNI, and tumor grade were analyzed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic significance of NLR, dNLR, PLR, LMR, PNI, and their paired combinations for glioma, particularly GBM.
A total of 750 patients with glioma (Grade I, 81 patients; Grade II, 208 patients; Grade III, 169 patients; Grade IV [GBM], 292 patients), 44 with acoustic neuroma, 271 with meningioma, 102 with nonlesional epilepsy, and 682 healthy controls were included in this study. Compared with healthy controls and patients with acoustic neuroma, meningioma, or nonlesional epilepsy, the patients with glioma had higher values of preoperative NLR and dNLR as well as lower values of LMR and PNI, whereas PLR was higher in glioma patients than in healthy controls and patients with nonlesional epilepsy. Subgroup analysis revealed a positive correlation between NLR, dNLR, PLR, and tumor grade but a negative correlation between LMR, PNI, and tumor grade in glioma. For glioma diagnosis, the area under the curve (AUC) obtained from the ROC curve was 0.722 (0.697–0.747) for NLR, 0.696 (0.670–0.722) for dNLR, 0.576 (0.549–0.604) for PLR, 0.760 (0.738–0.783) for LMR, and 0.672 (0.646–0.698) for PNI. The best diagnostic performance was obtained with the combination of NLR+LMR and dNLR+LMR, with AUCs of 0.777 and 0.778, respectively. Additionally, NLR (AUC 0.860, 95% CI 0.832–0.887), dNLR (0.840, 0.810–0.869), PLR (0.678, 0.641–0.715), LMR (0.837, 0.811–0.863), and PNI (0.740, 0.706–0.773) had significant predictive value for GBM compared with healthy controls and other disease groups. As compared with the Grade I–III glioma patients, the GBM patients had an AUC of 0.811 (95% CI 0.778–0.844) for NLR, 0.797 (0.763–0.832) for dNLR, 0.662 (0.622–0.702) for PLR, 0.743 (0.707–0.779) for LMR, and 0.661(0.622–0.701) for PNI. For the paired combinations, NLR+LMR demonstrated the highest accuracy.
The NLR+LMR combination was revealed as a noninvasive biomarker with relatively high sensitivity and specificity for glioma diagnosis, the differential diagnosis of glioma from acoustic neuroma and meningioma, GBM diagnosis, and the differential diagnosis of GBM from low-grade glioma.