Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke.

ABSTRACT:

Introduction

Early prognosis prediction of acute ischemic stroke (AIS) can support clinicians in choosing personalized treatment plans. The aim of this study is to develop a machine learning (ML) model that uses multiple post-labeling delay times (multi-PLD) arterial spin labeling (ASL) radiomics features to achieve early and precise prediction of AIS prognosis.

Methods

This study enrolled 102 AIS patients admitted between December 2020 and September 2024. Clinical data, such as age and baseline National Institutes of Health Stroke Scale (NIHSS) score, were collected. Radiomics features were extracted from cerebral blood flow (CBF) images acquired through multi-PLD ASL. Features were selected using least absolute shrinkage and selection operator regression, and three models

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11769822 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature