Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients.

ABSTRACT:

Background

Identifying the incidence and risk factors of Glioblastoma (GBM) and establishing effective predictive models will benefit the management of these patients.

Methods

Using GBM data from the Surveillance, Epidemiology, and End Results (SEER) database, we used Joinpoint software to assess trends in GBM incidence across populations of different age groups. Subsequently, we identified important prognostic factors by stepwise regression and multivariate Cox regression analysis, and established a Nomogram mathematical model. COX regression model combined with restricted cubic splines (RCS) model was used to analyze the relationship between tumor size and prognosis of GBM patients.

Results

The incidence of GBM has been on the rise since 1978, especially in the age

SUBMITTER: Mu Y 

PROVIDER: S-EPMC12008090 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature