Predictive Value of Temporal Muscle Thickness Measurements on Cranial Magnetic Resonance Images in the Prognosis of Patients With Primary Glioblastoma

Abstract

Objective: To investigate the predictive value of prognosis of primary GBM patients using TMT on three-dimensional (3D) MR images of the brain. Methods: Data of 130 patients with primary GBM from the TCGA-GBM database were analyzed retrospectively. TMT was measured on the axial plane by multi-planar reformation (MPR) of T1WI MR images perpendicular to the long axis of the temporal muscle at the level of the orbital roof. The axial MR plane was oriented parallel to the anterior commissure-posterior commissure line. Student's t-test or Mann–Whitney U-test was utilized to determine whether there were significant differences in the TMT and OS between male and female patients. The Pearson correlation analysis was adopted to evaluate the correlation between the age at GBM diagnosis and TMT. All patients were divided into two groups based on their median TMT, and the Kaplan–Meier curve was used to calculate the OS curve. The association between TMT and OS of GBM patients, as well as the multivariate analysis of TMT and other clinical factors affecting the survival time, was evaluated with Cox regression model. Results: TMT was a risk factor for the prognosis of GBM with its hazard ratio (HR) of 0.802 (95% CI 0.698–0.922; P = 0.002; Cox regression model). Grouped by median TMT, the group with above-median TMT demonstrated a significant increase in survival time (15.6 months) compared with the one with below-median TMT (11.2 months) (P < 0.001; log-rank test). In the multivariate survival analysis using a Cox regression model, TMT (HR 0.863; 95% CI 0.748–0.996; P = 0.044), age at the diagnosis of GBM (HR 1.042; 95% CI 1.024–1.060; P < 0.001), and concurrent chemoradiotherapy (HR 0.510; 95% CI 0.336–0.775; P = 0.002) were significantly associated with survival time. Conclusion: TMT as an independent predictor is sensitive to the survival prognosis of primary GBM patients, which has potential to predict the survival time.