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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Related Experiment Video

Updated: Sep 28, 2025

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
09:36

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A Radiomics Approach to Assess High Risk Carotid Plaques: A Non-invasive Imaging Biomarker, Retrospective Study.

Sihan Chen1, Changsheng Liu1, Xixiang Chen1

  • 1Department of Radiology, Renmin Hospital of Wuhan University and Hubei General Hospital, Wuhan, China.

Frontiers in Neurology
|March 30, 2022
PubMed
Summary

High-resolution MRI texture features enhance the non-invasive differentiation of symptomatic versus asymptomatic carotid plaques. This radiomics approach improves plaque vulnerability assessment, aiding in carotid atherosclerotic risk stratification.

Keywords:
HRMRIasymptomaticcarotid atherosclerosisradiomicssymptomatic

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Area of Science:

  • Radiology
  • Medical Imaging
  • Cardiovascular Research

Background:

  • Carotid atherosclerosis poses a significant risk for stroke.
  • Differentiating symptomatic plaques (SPs) from asymptomatic plaques (ASPs) is crucial for risk stratification.
  • Non-invasive methods for plaque characterization are highly desirable.

Purpose of the Study:

  • To develop a radiomics-based MRI sequence for non-invasive differentiation of SPs from ASPs.
  • To combine high-resolution magnetic resonance imaging (HRMRI) texture features with clinical factors for improved plaque assessment.
  • To evaluate the diagnostic performance of radiomics features in identifying vulnerable carotid plaques.

Main Methods:

  • Retrospective analysis of 115 patients with HRMRI data.
  • Segmentation and texture feature extraction from T2-weighted imaging (T2WI).
  • Feature selection using Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms.
  • Construction of a diagnostic model (radscore) incorporating T2WI texture features and clinical data.

Main Results:

  • LASSO identified 16 radiomics features related to plaque vulnerability.
  • A radscore model using eight texture features demonstrated superior performance compared to clinical information alone (AUCs: 0.923/0.989 vs. 0.713/0.735 in training/test sets).
  • The combined model of texture and clinical information achieved the highest diagnostic performance (AUCs: 0.926/0.898 in training/test sets).

Conclusions:

  • HRMRI-derived texture features offer significant incremental value for carotid atherosclerotic risk assessment.
  • Radiomics analysis of HRMRI can effectively differentiate between symptomatic and asymptomatic carotid plaques.
  • This non-invasive approach aids in assessing lesion vulnerability and guiding clinical management.