Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study.

ABSTRACT:

Background

Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies.

Methods

This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting s

SUBMITTER: Qiao W 

PROVIDER: S-EPMC10900633 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature