Prediction of prognosis in acute ischemic stroke after mechanical thrombectomy based on multimodal MRI radiomics and deep learning.

ABSTRACT:

Background

Acute ischemic stroke (AIS) is a major global health threat associated with high rates of disability and mortality, highlighting the need for early prognostic assessment to guide treatment. Currently, there are no reliable methods for the early prediction of poor prognosis in AIS, especially after mechanical thrombectomy. This study aimed to explore the value of radiomics and deep learning based on multimodal magnetic resonance imaging (MRI) in predicting poor prognosis in patients with AIS who underwent mechanical thrombectomy. This study aimed to provide a more accurate and comprehensive tool for stroke prognosis.

Methods

This study retrospectively analyzed the clinical data and multimodal MRI images of patients with stroke at admission. Logistic regression was

SUBMITTER: Pei L 

PROVIDER: S-EPMC12074947 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature