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Does Quantification of Carotid Plaque Surface Irregularities Better Detect Symptomatic Plaques Compared to the
Vasileios Rafailidis1, Ioannis Chryssogonidis1, Enrico Grisan2,3
1Department of Radiology, AHEPA University General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Color Doppler ultrasound (CDUS) and contrast-enhanced ultrasound (CEUS) reliably quantify carotid plaque irregularities. Quantitative measures, unlike subjective assessments, correlate with stroke risk in patients.
Area of Science:
- Vascular Imaging
- Cardiovascular Ultrasound
- Cerebrovascular Disease
Background:
- Carotid plaque surface irregularities are linked to stroke risk.
- Accurate quantification of these irregularities is crucial for risk stratification.
- Current imaging methods require evaluation for reliability and clinical correlation.
Purpose of the Study:
- To assess interobserver agreement of CDUS and CEUS in quantifying carotid plaque surface irregularities.
- To correlate objective (quantitative index) and subjective plaque assessments with stroke occurrence.
Main Methods:
- Observational study of 54 patients with 62 carotid plaques (31 symptomatic).
- Plaques with moderate to severe stenosis evaluated using CDUS and CEUS.
- Surface irregularities quantified via a surface irregularity index by two observers.
- Interobserver agreement and correlation with stroke occurrence analyzed.
Main Results:
- Both CDUS and CEUS demonstrated excellent interobserver agreement (ICC > 0.95).
- Irregular and ulcerated surfaces were observed in 69.4% of plaques.
- Quantitative surface irregularity index was significantly higher in symptomatic plaques (P < .05).
- Subjective plaque surface classification did not correlate with stroke occurrence (P > .05).
Conclusions:
- CDUS and CEUS provide reliable, reproducible quantification of carotid plaque surface irregularities.
- The quantitative surface irregularity index is a valuable objective measure associated with plaque symptoms.
- Subjective plaque assessment is less reliable for predicting stroke events compared to quantitative measures.
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