Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype

Lin, Andrew; Kolossváry, Márton; Yuvaraj, Jeremy; Cadet, Sebastien; McElhinney, Priscilla; Jiang, Canchen; Nerlekar, Nitesh; Nicholls, Stephen J.; Slomka, Piotr J.; Maurovich-Horvat, Pál; Wong, D.; Dey, Damini

doi:10.1016/j.jcmg.2020.06.033

Cited by 119 publications

(83 citation statements)

References 29 publications

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“…Kwiecinski et al [ 26 ] found that increased lesion-specific PCAT MA in patients with high-risk plaque was related to focal 18F-NaF PET uptake. Lin et al [ 27 ] reported on the relationship of PCAT radiomic features and PCAT MA in the proximal RCA and around (non-) culprit lesions at presentation and 6 months post-MI, in comparison to stable CAD and non-CAD cases. They report that the most significant radiomic parameters distinguishing patients with and without MI were based on texture and geometry, yielding information not included in PCAT attenuation.…”

Section: Discussionmentioning

confidence: 99%

Focal pericoronary adipose tissue attenuation is related to plaque presence, plaque type, and stenosis severity in coronary CTA

Assen

Ties

et al. 2021

Eur Radiol

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Objectives To investigate the association of pericoronary adipose tissue mean attenuation (PCATMA) with coronary artery disease (CAD) characteristics on coronary computed tomography angiography (CCTA). Methods We retrospectively investigated 165 symptomatic patients who underwent third-generation dual-source CCTA at 70kVp: 93 with and 72 without CAD (204 arteries with plaque, 291 without plaque). CCTA was evaluated for presence and characteristics of CAD per artery. PCATMA was measured proximally and across the most severe stenosis. Patient-level, proximal PCATMA was defined as the mean of the proximal PCATMA of the three main coronary arteries. Analyses were performed on patient and vessel level. Results Mean proximal PCATMA was −96.2 ± 7.1 HU and −95.6 ± 7.8HU for patients with and without CAD (p = 0.644). In arteries with plaque, proximal and lesion-specific PCATMA was similar (−96.1 ± 9.6 HU, −95.9 ± 11.2 HU, p = 0.608). Lesion-specific PCATMA of arteries with plaque (−94.7 HU) differed from proximal PCATMA of arteries without plaque (−97.2 HU, p = 0.015). Minimal stenosis showed higher lesion-specific PCATMA (−94.0 HU) than severe stenosis (−98.5 HU, p = 0.030). Lesion-specific PCATMA of non-calcified, mixed, and calcified plaque was −96.5 HU, −94.6 HU, and −89.9 HU (p = 0.004). Vessel-based total plaque, lipid-rich necrotic core, and calcified plaque burden showed a very weak to moderate correlation with proximal PCATMA. Conclusions Lesion-specific PCATMA was higher in arteries with plaque than proximal PCATMA in arteries without plaque. Lesion-specific PCATMA was higher in non-calcified and mixed plaques compared to calcified plaques, and in minimal stenosis compared to severe; proximal PCATMA did not show these relationships. This suggests that lesion-specific PCATMA is related to plaque development and vulnerability. Key Points • In symptomatic patients undergoing CCTA at 70 kVp, PCATMAwas higher in coronary arteries with plaque than those without plaque. • PCATMAwas higher for non-calcified and mixed plaques compared to calcified plaques, and for minimal stenosis compared to severe stenosis. • In contrast to PCATMAmeasurement of the proximal vessels, lesion-specific PCATMAshowed clear relationships with plaque presence and stenosis degree.

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Section: Discussionmentioning

confidence: 99%

Focal pericoronary adipose tissue attenuation is related to plaque presence, plaque type, and stenosis severity in coronary CTA

Assen

Ties

et al. 2021

Eur Radiol

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“…Their machine learning-based model performed better than the existing clinical risk factor model (Δ[C-statistic] = 0.126, p < 0.001) in predicting the outcome of MACEs. Lin et al explored the ability of the radiomics signature of the PCAT in CCTA images to discriminate patients with MI from those with stable or no coronary artery disease (Lin et al 2020 ). Using an XGBoost that combined clinical factors, PCAT attenuation, and radiomics features, their method significantly improved the discrimination ability of acute MI (AUC = 0.87) compared with a model with clinical factors and PCAT attenuation (AUC = 0.77) or clinical factors alone (AUC = 0.76).…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the FAI that simply represents the CT value, radiomics can mine the high-throughput quantitative information of the PCAT. Oikonomou et al reported that the radiomics signature of the PCAT was associated with the fibrosis and vascularity of the PCAT, and the PCAT represented the adipose tissue remodelling caused by coronary inflammation (Oikonomou et al 2019 (Lin et al 2020). Using an XGBoost that combined clinical factors, PCAT attenuation, and radiomics features, their method significantly improved the discrimination ability of acute MI (AUC = 0.87) compared with a model with clinical factors and PCAT attenuation (AUC = 0.77) or clinical factors alone (AUC = 0.76).…”

Section: Clinical Applications Of Machine Learning In Pcatmentioning

confidence: 99%

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Zhang

Sun²,

Jiang

et al. 2021

European J Hybrid Imaging

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Background Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue. Methods We exhaustively searched PubMed and the Web of Science for publications prior to 30 April 2021. The search included eligible studies that used AI for image analysis of epicardial adipose tissue (EAT) or pericoronary adipose tissue (PCAT). The risk of bias and concerns regarding applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results Of the 140 initially identified citation records, 19 high-quality studies were eligible for this systematic review, including 15 (79%) on the image segmentation and quantification of EAT or PCAT and 4 (21%) on the clinical application of EAT or PCAT in cardiovascular diseases. All 19 included studies were rated as low risk of bias in terms of flow and timing, reference standards, and the index test and as having low concern of applicability in terms of reference standards and patient selection, but 16 (84%) studies did not conduct external validation. Conclusion AI technology can provide accurate and quicker methods to segment and quantify EAT and PCAT images and shows potential value in the diagnosis and risk prediction of cardiovascular diseases. AI is expected to expand the value of cardiac adipose tissue imaging.

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“…Finally, two other studies used a combined AI powered radiomics approach to demonstrate the incremental value of assessing PCAT attenuation over traditional CCTA based cardiovascular risk prediction tools ( 25 , 26 ).…”

Section: Ai Applications In Cad Pre-test Likelihood Definitionmentioning

confidence: 99%

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Maragna

Giacari

Guglielmo

et al. 2021

Front. Cardiovasc. Med.

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Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.

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Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype

Cited by 119 publications

References 29 publications

Focal pericoronary adipose tissue attenuation is related to plaque presence, plaque type, and stenosis severity in coronary CTA

Focal pericoronary adipose tissue attenuation is related to plaque presence, plaque type, and stenosis severity in coronary CTA

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

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