A novel CT-based edema grading system combined with machine learning for precise prognostic prediction in traumatic brain injury.

ABSTRACT:

Background

Post-traumatic brain edema is a critical factor influencing the prognosis of traumatic brain injury (TBI). However, current imaging scoring systems, such as the Marshall and Rotterdam scores, lack specific quantitative criteria for assessing brain edema. This study aims to develop a prognostic prediction model for TBI by integrating the novel A5(+ 1) CT brain edema grading system with advanced machine learning methodologies.

Methods

A total of 216 patients with traumatic brain injury (TBI) were retrospectively enrolled in this study. CT imaging characteristics and clinical parameters within 72 h post-injury were extracted. Variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, nine machine

SUBMITTER: Lv Y 

PROVIDER: S-EPMC12599053 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature