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Related Experiment Video

Updated: Aug 21, 2025

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

Diffusion-weighted imaging-based radiomics for predicting 1-year ischemic stroke recurrence.

Hao Wang¹, Yi Sun¹, Jie Zhu¹

¹Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China.

Frontiers in Neurology

|November 17, 2022

Summary

This summary is machine-generated.

Diffusion-weighted imaging (DWI) radiomics can predict 1-year ischemic stroke recurrence. Combining radiomic and clinical data improved prediction accuracy for stroke recurrence, offering valuable insights for patient management.

Keywords:

diffusion magnetic resonance imaging radiology recurrence stroke

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Last Updated: Aug 21, 2025

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Area of Science:

Neurology
Radiology
Medical Imaging

Background:

Ischemic stroke recurrence poses a significant health risk.
Predictive models for stroke recurrence are crucial for timely intervention.
Diffusion-weighted imaging (DWI) offers insights into tissue characteristics.

Purpose of the Study:

To investigate the utility of radiomics derived from DWI for predicting 1-year ischemic stroke recurrence.
To compare the predictive performance of radiomics-based models against clinical data alone.

Main Methods:

Retrospective analysis of 1,003 ischemic stroke patients.
Lesion segmentation using a 2D U-Net automatic network on DWI.
Radiomics feature extraction from DWI and ADC maps, followed by feature selection and RNN classification.

Main Results:

The radiomics-based model achieved an AUC of 0.779.
A combined model incorporating clinical and radiomic features yielded a superior AUC of 0.847.
Older age and specific stroke subtypes (cardioembolic, large artery atherosclerosis) were associated with higher recurrence rates.

Conclusions:

DWI-based radiomics show promise in predicting 1-year ischemic stroke recurrence.
Integrating radiomic features with clinical data enhances prediction accuracy.
This approach can aid in identifying high-risk patients for targeted secondary prevention strategies.