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Data-driven atherosclerotic plaque profiles from QAngio-CT associated with functional impairments assessed with
Erick Alexanderson-Rosas1,2, Leopoldo Pérez-de Isla3, Neftali E Antonio-Villa4
1Department of Nuclear Cardiology, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico.
Insights
This study identified three atherosclerotic plaque clusters in coronary artery disease (CAD) patients. These plaque types correlate with myocardial dysfunction, offering insights into CAD progression and characterization.
Area of Science:
- Cardiology
- Medical Imaging
- Computational Biology
Background:
- Coronary artery disease (CAD) involves progressive atherosclerotic plaque dysfunction.
- Characterizing atherosclerotic plaque clusters and their impact on myocardial function requires further exploration.
Purpose of the Study:
- To identify data-driven atherosclerotic plaque clusters using coronary computed tomography angiography.
- To correlate identified plaque types with myocardial dysfunction assessed by positron emission tomography/computed tomography (PET-CT).
Main Methods:
- Analysis of myocardial perfusion imaging in high-risk subjects for CAD.
- Semi-automated atherosclerotic plaque characterization using K-mean clustering.
- Correlation of an atherosclerotic plaque score with principal component analysis (PCA) scores from PET-CT data.
Main Results:
- Three distinct atherosclerotic plaque clusters were identified: fibro-necrotic, fibrous, and necro-calcified.
- Atherosclerotic plaque scores significantly correlated with PCA components reflecting impaired ventricular function, dyssynchrony, and calcium scores.
- Plaque characteristics were linked to myocardial dysfunction parameters evaluated by PET-CT.
Conclusions:
- Identified atherosclerotic plaque clusters offer a pathophysiological basis for understanding CAD progression.
- This clustering approach may enhance the characterization of developing coronary artery disease.
Objective:
Although coronary artery disease (CAD) involves progressive atherosclerotic plaque dysfunction, further characterization of different cluster profiles and their impact on myocardial function has not been fully explored. Therefore, this study's pilot aim is to identify data-driven atherosclerotic plaque clusters using coronary computed tomography angiography and correlate types with dysfunction observed by positron emission tomography/computed tomography (PET-CT).
Methods:
Myocardial perfusion study images of subjects with a high clinical risk of developing CAD were analyzed with semi-automated atherosclerotic plaque characterization. The K-mean clustering method was performed to detect coronary atherosclerotic plaques. An atherosclerotic plaque score was assigned to each subject according to their arteries cluster profiles and was correlated with a principal component analysis (PCA) score that evaluated myocardial perfusion, volumes, and synchrony from PET-CT.
Results:
Three well-differentiated clusters were identified from 95 coronary atherosclerotic plaques of 35 subjects. Cluster 1 (28.2%) was characterized by a high fibro-necrotic atherosclerotic plaque, cluster 2 (59.7%) by predominantly fibrous atherosclerotic plaque, and cluster 3 (11.9%) by a necro-calcified atherosclerotic plaque. The atherosclerotic plaque score displayed a significant correlation with the first (r = 0.42; 95% confidence interval [CI]: 0.049-0.675, p = 0.012) and third (r = 0.36; 95% CI: 0.036-0.626, p = 0.035) PCA score components. These were comprised parameters related to impairments in ventricular volume capacity, mechanical dyssynchrony, filling rates, and calcium scores in PET-CT evaluation.
Conclusion:
The atherosclerotic plaque clusters identified in this study could provide a pathophysiological explanation of CAD progression and potentially lead to a better characterization of the developing disease.
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