Radiomics-based machine learning model for predicting secondary decompressive craniectomy in TBI patients after emergent craniotomy with bone flap replacement.

ABSTRACT:

Background

Secondary decompressive craniectomy (DC) is commonly integrated into tiered therapeutic protocols in the intensive care unit (ICU) to manage elevated intracranial pressure following traumatic brain injury (TBI). Identifying high-risk patients in advance could enable early intervention and help prevent further deterioration. This study aims to develop a machine learning-based predictive model using radiomics to assess the likelihood of secondary DC in TBI patients.

Methods

A total of 65 patients were enrolled and divided into training and test cohorts through stratified random sampling with a 7:3 ratio. Radiomic features were extracted from pre-evacuation CT data. The most relevant features were identified through importance score computation, and various predictiv

SUBMITTER: Chen T 

PROVIDER: S-EPMC12781376 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature