Key result
Biomechanical cap stress computation showed fair agreement (κ=0.39) with histopathologic classification for identifying vulnerable carotid plaques, with disagreement in 41% of plaques.
Why the study?
Does biomechanical cap stress computation agree with histopathologic classification for identifying vulnerable carotid plaques?
Cross-Sectional (n=34)
Does biomechanical cap stress computation agree with histopathologic classification for identifying vulnerable carotid plaques?
Effect estimate: κ=0.39
Due to limited MRI resolution, noninvasive identification of stable, thick-cap plaques is more reliable than targeting vulnerable plaques to reduce surgical interventions.
Biomechanical stress computation should not yet identify vulnerable plaques; leaves open reliable noninvasive detection of stable plaques to reduce interventions.
BACKGROUND AND PURPOSE: Two approaches to target plaque vulnerability-a histopathologic classification scheme and a biomechanical analysis-were compared and the implications for noninvasive risk stratification of carotid plaques using magnetic resonance imaging were assessed. METHODS: Seventy-five histological plaque cross sections were obtained from carotid endarterectomy specimens from 34 patients (>70% stenosis) and subjected to both a Virmani histopathologic classification (thin fibrous cap atheroma with <0.2-mm cap thickness, presumed vulnerable) and a peak cap stress computation (<140 kPa: presumed stable; >300 kPa: presumed vulnerable). To demonstrate the implications for noninvasive plaque assessment, numeric simulations of a typical carotid magnetic resonance imaging protocol were performed (0.62×0.62 mm(2) in-plane acquired voxel size) and used to obtain the magnetic resonance imaging-based peak cap stress. RESULTS: Peak cap stress was generally associated with histological classification. However, only 16 of 25 plaque cross sections could be labeled as high-risk (peak cap stress>300 kPa and classified as a thin fibrous cap atheroma). Twenty-eight of 50 plaque cross sections could be labeled as low-risk (a peak cap stress<140 kPa and not a thin fibrous cap atheroma), leading to a κ=0.39. 31 plaques (41%) had a disagreement between both classifications. Because of the limited magnetic resonance imaging voxel size with regard to cap thickness, a noninvasive identification of only a group of low-risk, thick-cap plaques was reliable. CONCLUSIONS: Instead of trying to target only vulnerable plaques, a more reliable noninvasive identification of a select group of stable plaques with a thick cap and low stress might be a more fruitful approach to start reducing surgical interventions on carotid plaques.
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Gijsen et al. (2015) conducted a cross-sectional in Carotid plaques (>70% stenosis) (n=34). Biomechanical cap stress computation vs. Virmani histopathologic classification was evaluated on Agreement between biomechanical cap stress and histological classification for plaque vulnerability (κ=0.39). Biomechanical cap stress computation showed fair agreement (κ=0.39) with histopathologic classification for identifying vulnerable carotid plaques, with disagreement in 41% of plaques.
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