Predicting the clinical prognosis of acute ischemic stroke using machine learning: an application of radiomic biomarkers on non-contrast CT after intravascular interventional treatment.

ABSTRACT:

Purpose

This study aimed to develop a radiomic model based on non-contrast computed tomography (NCCT) after interventional treatment to predict the clinical prognosis of acute ischemic stroke (AIS) with large vessel occlusion.

Methods

We retrospectively collected 141 cases of AIS from 2016 to 2020 and analyzed the patients' clinical data as well as NCCT data after interventional treatment. Then, the total dataset was divided into training and testing sets according to the subject serial number. The cerebral hemispheres on the infarct side were segmented for radiomics signature extraction. After radiomics signatures were standardized and dimensionality reduced, the training set was used to construct a radiomics model using machine learning. The testing set was then used to va

SUBMITTER: Gu H 

PROVIDER: S-EPMC11374607 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature