Investigation of the Relationship Between the Degree of Peritumoral Brain Edema and Pathological Features of Glioma Before Surgery

Abstract

Background: Gliomas are the most common malignant tumors of the central nervous system (CNS). Preoperative prediction of the malignancy grade of gliomas are of particular importance. These tumors are often accompanied by peritumoral brain edema (PTBE). Previous studies have suggested that the degree of PTBE is an independent indicator of the prognosis of gliomas. Objectives: This study aimed to investigate the relationships between the degree of PTBE and the grade of glioma, isocitrate dehydrogenase 1 (IDH1) mutation status, and Ki-67 expression level in gliomas. Patients and Methods: In this retrospective cross-sectional study, a total of 82 patients were enrolled, according to the 2016 World Health Organization (WHO) classification of CNS tumors. Overall, 29 tumors were pathologically confirmed as low-grade gliomas (LGGs , grade I-II), whereas the remaining 53 tumors were classified as high-grade gliomas (HGGs, grade III-IV). The IDH1 mutations, Ki-67 expression, and magnetic resonance imaging (MRI) findings were retrospectively analyzed. The tumor and tumor + PTBE volumes were also measured, and the tumor edema index (EI) was calculated for each patient. Edema was then graded and correlated with the pathological parameters. Results: The degree of EI was higher in the HGG group compared to the LGG group, and the difference was statistically significant (z = -7.018, P < 0.05). Besides, the degree of EI was higher in the IDH1 wild-type compared with mutant groups (z = -4.116, P < 0.05). The degree of EI significantly associated with Ki-67 expression and patient’s age (P < 0.05), whereas there was no significant association between the degree of EI and gender (z = -0.497, P = 0.619). The Spearman’s correlation test revealed that the EI degree was positively correlated with the Ki-67 expression level and age, with correlation coefficients of 0.740 and 0.466, respectively. Moreover, the multivariate logistic regression analysis indicated that EI and IDH1 had significant effects on differentiating LGGs from HGGs (P < 0.05 for both). The receiver operating characteristic (ROC) curve analysis showed that EI was an optimal index for differentiating LGGs from HGGs, with an area under curve (AUC) of 0.822 (cutoff value: 1.722, sensitivity: 95.8%, specificity: 70.0%, 95% CI: 0.718 - 0.899). Conclusion: The degree of PTBE was found to be a valuable index for the differential diagnosis of LGGs from HGGs. It has a significant difference between IDH1 wild and mutation status, furthermore, it was positively correlated with the age and Ki-67 level.