Radiomics signature and deep learning signature of intrathrombus and perithrombus for prediction of malignant cerebral edema after acute ischemic stroke: a multicenter CT study.

ABSTRACT:

Objectives

To accurately assess the predictive ability of radiomics and deep learning (DL) features in intrathrombus and perithrombus regions for the risk of malignant cerebral edema (MCE) after acute ischemic stroke (AIS).

Materials and methods

A retrospective study was conducted, enrolling 406 AIS patients who underwent admission CT before endovascular thrombectomy (EVT). Center A patients were randomly divided (7:3) into training/testing sets; Centers B and C formed the external validation cohort. Regions of interest (ROIs) of thrombus and perithrombus were manually delineated and automatically expanded in margin by one pixel. Four hundred twenty-eight radiomic features were extracted from CT images of intrathrombus and perithrombus regions, and 128 DL features were obtai

SUBMITTER: Wang S 

PROVIDER: S-EPMC12457144 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature