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Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: A Deep Learning-Based Analysis of
Yin Guo1, Ebru Yaman Akcicek2, Daniel S Hippe3
1Department of Bioengineering, University of Washington, Seattle, WA, USA.
Insights
Intraplaque hemorrhage (IPH) in carotid atherosclerosis significantly accelerates plaque growth over time, especially with larger hemorrhage volumes. Arteries without IPH showed minimal progression, highlighting IPH as a key driver of carotid plaque burden increase.
Area of Science:
- Cardiovascular Imaging
- Neurology
- Medical Artificial Intelligence
Background:
- Carotid atherosclerosis is a primary cause of ischemic stroke.
- Intraplaque hemorrhage (IPH) is linked to increased stroke risk and plaque burden.
- Long-term effects of IPH on carotid plaque dynamics require further investigation.
Purpose of the Study:
- To evaluate the long-term impact of IPH on carotid plaque burden progression.
- Utilize deep learning-based segmentation on multi-contrast magnetic resonance vessel wall imaging (VWI).
Main Methods:
- Developed and validated deep learning pipelines for carotid vessel wall and IPH segmentation.
- Analyzed plaque progression in 28 asymptomatic subjects over approximately 5.8 years with an average of 4.7 VWI scans.
- Employed generalized estimating equations and linear mixed-effects models to assess IPH's association with plaque burden progression.
Main Results:
- IPH was detected in 23/50 arteries; new or persistent IPH occurred in a significant proportion of arteries.
- Arteries without IPH showed minimal progression (-0.001%/year).
- IPH presence or development was associated with a 2.3% absolute increase in plaque burden (%WV) (p < 0.001), with larger IPH volumes showing a positive correlation (p = 0.005).
Conclusions:
- Deep learning segmentation effectively quantified IPH and its impact on carotid plaque burden.
- IPH can persist long-term and significantly accelerates carotid plaque growth.
- IPH presence and volume are critical factors in long-term carotid plaque progression, contrasting with minimal progression in arteries without IPH.
Background:
Carotid atherosclerosis is a major contributor in the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke risk and plaque burden, its long-term effects on plaque dynamics remain unclear. This study aimed to evaluate the long-term impact of IPH on carotid plaque burden progression using deep learning-based segmentation on multi-contrast magnetic resonance vessel wall imaging (VWI).
Methods:
Twenty-eight asymptomatic subjects with carotid atherosclerosis underwent an average of 4.7 ± 0.6 VWI scans over 5.8 ± 1.1 years. Deep learning pipelines were developed and validated to segment the carotid vessel walls and IPH. Bilateral plaque progression was analyzed using generalized estimating equations, and linear mixed-effects models evaluated long-term associations between IPH occurrence, IPH volume, and plaque burden (%WV) progression.
Results:
IPH was detected in 23/50 of arteries. Of arteries without IPH at baseline, 11/39 developed new IPH that persisted, while 5/11 arteries with baseline IPH exhibited it throughout the study. Bilateral plaque growth was significantly correlated (r = 0.54, p < 0.001), but this symmetry was weakened with IPH presence. The progression rate for arteries without IPH was -0.001 %/year (p = 0.90). However, IPH presence or development at any point was associated with a 2.3% absolute increase in %WV on average (p < 0.001). The volume of IPH was also positively associated with increased %WV (p = 0.005).
Conclusions:
Deep learning-based segmentation pipelines were utilized to identify IPH, quantify IPH volume, and measure their effects on carotid plaque burden during long-term follow-up. Findings demonstrated that IPH may persist for extended periods. While arteries without IPH demonstrated minimal progression under contemporary treatment, presence of IPH and greater IPH volume significantly accelerated long-term plaque growth.
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