Statistical Methods
The chi-squared test was used to compare continuous variables (rates) and categorical variables (histological grades and stage of gliomas). Kaplan–Meier plots were used for the determining the survival difference of variables. Cox regression was used for identifying risk factors of prognosis and included univariate and multivariate analyses Clinically important variables that showed significance (P<0.1) in the univariate analysis formed the input for multivariate analysis using the Cox risk regression model with backward elimination. Variables that showed significance (P<0.0001) in the multivariate analysis were selected for developing a nomogram. CSS was determined for 1, 3 and 5-years. Accuracy of the nomogram was assessed using C-index and calibration curves generated following 1000 bootstrap resampling. A decision curve analysis is a net benefit analysis that compares the true-positive to the weighted false-positive rates across different risk thresholds that a clinician/patient might want to accept. Based on the median value of the total scores in the nomogram, a risk stratification model was built and comprised patients who were divided into two prognostic groups. All statistical analyses were performed using R and Empower Stats [www.empowerstats.com, XY Solutions, Inc. Boston MA]. A two-tailed P value <0.05 was considered statistically significant.