Pericoronary Adipose Tissue Computed Tomography Attenuation and High-Risk Plaque Characteristics in Acute Coronary Syndrome Compared With Stable Coronary Artery Disease | Cardiology | JAMA Cardiology | JAMA Network
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Figure.  Example of How to Measure the Pericoronary Adipose Tissue and Plaque Characteristics of a Culprit Lesion
Example of How to Measure the Pericoronary Adipose Tissue and Plaque Characteristics of a Culprit Lesion

Quantification of coronary plaques and pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation of a culprit lesion in the mid left anterior descending coronary artery. A, Axial view and range of Hounsfield units (HU) to detect pericoronary fat (PCAT color map ranging from bright yellow [−30 HU] to dark red [−190 HU]). B, Cross-section and straightened view of PCAT measure. C, Cross-section and straightened view of plaque measure (noncalcified plaque highlighted in red). D, Curved multiplane review of PCAT measure.

Table 1.  Demographic, Clinical, and Plaque Characteristics of the Study Populationa
Demographic, Clinical, and Plaque Characteristics of the Study Populationa
Table 2.  Quantitative and Qualitative Analysis of Culprit Lesions in Patients With ACS vs Nonculprit Lesions in Patients With ACS and vs Highest-Grade Stenosis Lesions in Control Patients With Stable CADa
Quantitative and Qualitative Analysis of Culprit Lesions in Patients With ACS vs Nonculprit Lesions in Patients With ACS and vs Highest-Grade Stenosis Lesions in Control Patients With Stable CADa
1.
Ross  R.  Atherosclerosis—an inflammatory disease.  N Engl J Med. 1999;340(2):115-126. doi:10.1056/NEJM199901143400207PubMedGoogle ScholarCrossref
2.
Fishbein  MC, Siegel  RJ.  How big are coronary atherosclerotic plaques that rupture?  Circulation. 1996;94(10):2662-2666. doi:10.1161/01.CIR.94.10.2662PubMedGoogle ScholarCrossref
3.
Fleg  JL, Stone  GW, Fayad  ZA,  et al.  Detection of high-risk atherosclerotic plaque: report of the NHLBI Working Group on current status and future directions.  JACC Cardiovasc Imaging. 2012;5(9):941-955. doi:10.1016/j.jcmg.2012.07.007PubMedGoogle ScholarCrossref
4.
Cheng  VY, Slomka  PJ, Le Meunier  L,  et al.  Coronary arterial 18F-FDG uptake by fusion of PET and coronary CT angiography at sites of percutaneous stenting for acute myocardial infarction and stable coronary artery disease.  J Nucl Med. 2012;53(4):575-583. doi:10.2967/jnumed.111.097550PubMedGoogle ScholarCrossref
5.
Dweck  MR, Fayad  ZA.  Imaging: perivascular fat—an unheralded informant of coronary inflammation.  Nat Rev Cardiol. 2017;14(10):573-574. doi:10.1038/nrcardio.2017.127PubMedGoogle ScholarCrossref
6.
Antonopoulos  AS, Sanna  F, Sabharwal  N,  et al.  Detecting human coronary inflammation by imaging perivascular fat.  Sci Transl Med. 2017;9(398):eaal2658. doi:10.1126/scitranslmed.aal2658PubMedGoogle ScholarCrossref
7.
Dey  D, Achenbach  S, Schuhbaeck  A,  et al.  Comparison of quantitative atherosclerotic plaque burden from coronary CT angiography in patients with first acute coronary syndrome and stable coronary artery disease.  J Cardiovasc Comput Tomogr. 2014;8(5):368-374. doi:10.1016/j.jcct.2014.07.007PubMedGoogle ScholarCrossref
8.
Hell  MM, Motwani  M, Otaki  Y,  et al.  Quantitative global plaque characteristics from coronary computed tomography angiography for the prediction of future cardiac mortality during long-term follow-up.  Eur Heart J Cardiovas Imaging. 2017;18(12):1331-1339. doi:10.1093/ehjci/jex183PubMedGoogle ScholarCrossref
9.
Motoyama  S, Ito  H, Sarai  M,  et al.  Plaque characterization by coronary computed tomography angiography and the likelihood of acute coronary events in mid-term follow-up.  J Am Coll Cardiol. 2015;66(4):337-346. doi:10.1016/j.jacc.2015.05.069PubMedGoogle ScholarCrossref
10.
Pflederer  T, Marwan  M, Schepis  T,  et al.  Characterization of culprit lesions in acute coronary syndromes using coronary dual-source CT angiography.  Atherosclerosis. 2010;211(2):437-444. doi:10.1016/j.atherosclerosis.2010.02.001PubMedGoogle ScholarCrossref
11.
Puchner  SB, Liu  T, Mayrhofer  T,  et al.  High-risk plaque detected on coronary CT angiography predicts acute coronary syndromes independent of significant stenosis in acute chest pain: results from the ROMICAT-II trial.  J Am Coll Cardiol. 2014;64(7):684-692. doi:10.1016/j.jacc.2014.05.039PubMedGoogle ScholarCrossref
12.
Nair  A, Kuban  BD, Tuzcu  EM, Schoenhagen  P, Nissen  SE, Vince  DG.  Coronary plaque classification with intravascular ultrasound radiofrequency data analysis.  Circulation. 2002;106(17):2200-2206. doi:10.1161/01.CIR.0000035654.18341.5EPubMedGoogle ScholarCrossref
13.
Lu  MT, Park  J, Ghemigian  K,  et al.  Epicardial and paracardial adipose tissue volume and attenuation—Association with high-risk coronary plaque on computed tomographic angiography in the ROMICAT II trial.  Atherosclerosis. 2016;251:47-54. doi:10.1016/j.atherosclerosis.2016.05.033PubMedGoogle ScholarCrossref
14.
Okubo  R, Nakanishi  R, Toda  M,  et al.  Pericoronary adipose tissue ratio is a stronger associated factor of plaque vulnerability than epicardial adipose tissue on coronary computed tomography angiography.  Heart Vessels. 2017;32(7):813-822. doi:10.1007/s00380-017-0943-1PubMedGoogle ScholarCrossref
Brief Report
September 2018

Pericoronary Adipose Tissue Computed Tomography Attenuation and High-Risk Plaque Characteristics in Acute Coronary Syndrome Compared With Stable Coronary Artery Disease

Author Affiliations
  • 1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
  • 2Faculty of Medicine, Department of Cardiology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
  • 3Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California
  • 4Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
JAMA Cardiol. 2018;3(9):858-863. doi:10.1001/jamacardio.2018.1997
Key Points

Question  Are high-risk plaque characteristics from coronary computed tomography (CT) angiography associated with pericoronary adipose tissue (PCAT) CT attenuation in patients with a first acute coronary syndrome and matched controls with stable coronary artery disease?

Findings  In this case-control study, culprit lesions showed the highest burden of low- and intermediate-attenuation noncalcified plaque (NCP) and the highest PCAT CT attenuation. Only low- and intermediate-attenuation NCP burden and PCAT CT attenuation were associated with the presence of culprit lesions.

Meaning  Combined quantitative adverse plaque features and PCAT CT attenuation may allow for a more reliable identification of vulnerable plaques.

Abstract

Importance  Pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation measured from coronary CT angiography (CTA) may be a promising metric in identifying high-risk plaques.

Objective  To determine whether high-risk plaque characteristics from coronary CTA are associated with PCAT CT attenuation in patients with a first acute coronary syndrome (ACS) and matched controls with stable coronary artery disease (CAD).

Design, Setting, and Participants  This retrospective, single-center case-control study (data were acquired at the University of Erlangen from 2009-2010) analyzed the CTA data sets of 19 patients who presented with ACS and 16 controls with stable CAD who were matched based on sex, age, and risk factors. Study observers were blinded to patients’ clinical data. Semiautomated software was used to quantify and characterize plaques. The CT attenuation (Hounsfield unit [HU]) of PCAT was automatically measured around all lesions.

Main Outcomes and Measures  To investigate the association between high-risk plaque characteristics from CTA and PCAT CT attenuation as a novel surrogate measure of coronary inflammation.

Results  A total of 35 patients (mean [SD] age, 59.5 [11.3] years; 30 men [86%] and 5 women [14%]) were included in the analysis. Low- and intermediate-attenuation noncalcified plaque (NCP) burden were increased in culprit lesions (n = 19) compared with both nonculprit lesions (n = 55) in patients with ACS (12.6% vs 3.6%; P < .001; 38.4% vs 19.4%; P < .001) and the control group’s highest-grade stenosis lesions (n = 16) (12.6% vs 5.6%; P = .002; 38.4% vs 22.1%; P < .001). Pericoronary adipose tissue attenuation was increased around culprit lesions (n = 19) compared with nonculprit lesions (n = 55) in patients with ACS (−69.1 HU vs −74.8 HU; P = .01) and highest-grade stenosis lesions in control patients (n = 16) (−69.1 HU vs −76.4 HU; P = .01). Pericoronary adipose tissue CT attenuation of all lesions in patients with ACS (n = 74) correlated more strongly with intermediate-attenuation (r = 0.393; P = .001) over low-attenuation (r = 0.221; P = .06) and high-attenuation NCP burden (r = −0.103; P = .38). In a multivariable analysis, low- and intermediate-attenuation NCP burden and PCAT CT attenuation were independently associated with the presence of culprit lesions (P < .05).

Conclusions and Relevance  Pericoronary CT attenuation was increased around culprit lesions compared with nonculprit lesions of patients with ACS and the lesions of matched controls. Combined quantitative high-risk plaque features and PCAT CT attenuation may allow for a more reliable identification of vulnerable plaques.

Introduction

Vascular inflammation drives the development of coronary atherosclerosis and the rupture of vulnerable plaques, resulting in acute coronary syndrome (ACS).1 Most high-risk plaques are nonobstructive and may not be detected with myocardial perfusion imaging tests, such as cardiac magnetic resonance imaging, single-photon emission computed tomography (CT), or echocardiography, as these stress tests assess stress-induced myocardial ischemia that is associated with obstructive coronary stenosis.2 Standard noninvasive approaches are unable to measure coronary inflammation.3 With fluorine 18–labeled (18F) fluorodeoxyglucose positron emission tomography (PET), the detection of coronary inflammation is challenging and requires a PET scan with complex imaging protocols and processing.4 Thus, the noninvasive detection and identification of inflamed coronary plaques remains challenging in clinical practice.5

Epicardial coronary arteries are encased with pericoronary adipose tissue (PCAT). Recently, PCAT CT attenuation measured from CT angiography (CTA) was able to detect biopsy result–proven vascular inflammation in patients undergoing cardiac surgery.6 To our knowledge, the association between high-risk plaque characteristics and PCAT CT attenuation in preintervention in vivo culprit lesions is unknown. We investigated the direct per-lesion association between anatomical plaque characteristics and inflammation by PCAT CT attenuation to improve vulnerable plaque identification in patients at risk of developing ACS.

Methods
Patients

The study design has been reported previously.7 Institutional review board approval was obtained by the University of Erlangen and patients provided written informed consent. Additionally, the institutional review board approval for plaque quantification from the anonymized data was obtained at Cedars-Sinai Medical Center. In brief, we analyzed consecutive 19 patients who presented with a first ACS who underwent CTA that was followed by invasive angiography, and compared with controls with stable coronary artery disease (CAD) matched by age decile, sex, and risk factors; the controls also underwent coronary CTA followed by invasive angiography (eMethods in the Supplement).

Coronary Plaque Analysis

The CT imaging protocol and analysis of the coronary plaques were described previously (eMethods in the Supplement).7 An experienced independent reader who was masked to the patient data analyzed all coronary segments with a lumen diameter of 2 mm or greater using semiautomated software (Autoplaque, version 2.0; Cedar-Sinai Medical Center). The plaque measurements included absolute volumes (in millimeters cubed) and the corresponding burden (plaque volume × 100% / vessel volume) of calcified plaques and noncalcified plaques (NCP), as well as the remodeling index, plaque length, contrast density difference (CDD), and diameter stenosis. Noncalcified plaques were further divided into their components: low- (−30 to 30 Hounsfield Units [HU]), intermediate- (31-130 HU), and high-attenuation NCP (131-350 HU) volumes, and the corresponding plaque burden.

Analysis of PCAT

For each lesion, PCAT was sampled radially outwards from the outer vessel wall and measured as voxels, with attenuation between −190 HU and −30 HU. Pericoronary adipose tissue CT attenuation was defined as the average CT attenuation of adipose tissue within the defined volume of interest (Figure).6 We considered PCAT CT attenuation within an outer radial distance from the vessel wall equal to the average diameter of the lesion (Figure).6

Statistical Analysis

The statistical analysis was performed using SPSS, version 24 (IBM). The presence of normal distribution for continuous data was tested with the Shapiro-Wilk test. A 2-sample t test or Wilcoxon rank sum test were applied to compare differences in continuous variables between groups, while Pearson or Spearman rank correlations were used to assess correlations between continuous variables. The association of plaque burden and PCAT CT attenuation with culprit lesions was assessed using a multivariable stepwise-backward logistic regression while adjusting for age, sex, and the number of risk factors. Optimal PCAT CT attenuation thresholds were determined from a receiver operator characteristic analysis, at the point in which the Youden J statistic (J = sensitivity + specificity− 1) was the highest. A 2-sided P value of <.05 was considered significant.

Results
Patients

Table 1 shows the characteristics of the included 35 patients (mean [SD] age, 59.5 [11.3] years; 30 men [86%]; mean [SD] number of risk factors, 2.1 [1]). Risk factors and total plaque distribution did not differ significantly between patients with ACS and controls (Table 1).

Per-Patient Coronary Plaque Analysis

Patients with ACS had less high-attenuation NCP burden compared with controls (Table 1); all other plaque burdens did not differ significantly between the 2 groups (Table 1). Intermediate- and low-attenuation NCP volume, as well as the stenosis grade of lesions and CDD, were significantly higher in the ACS group (Table 1).

Culprit vs Nonculprit Lesion Anatomical Characteristics

Plaque and PCAT measurements were performed in 19 culprit lesions of patients with ACS and in 105 nonculprit lesions in the ACS and control groups. Among patients with ACS, the low-attenuation NCP volume and the burdens of NCP, intermediate-attenuation NCP, and low-attenuation NCP were significantly higher in culprit vs nonculprit lesions (Table 2; eFigure 1 in the Supplement). Culprit lesions had higher stenosis and CDD and shorter plaque length (Table 2).

Between patients with ACS and controls, low-attenuation NCP volume was significantly higher and high-attenuation NCP volume significantly lower in culprit lesions compared with the highest-grade stenosis in controls (Table 2). The burden of total plaque, NCP, and intermediate- and low-attenuation NCP were significantly higher in culprit lesions (Table 2; eFigure 1 in the Supplement; 3-dimensional example of PCAT quantification shown in eFigure 3 of the Supplement). Culprit lesions also had higher maximal stenosis and CDD (Table 2).

Culprit vs Nonculprit Lesion Inflammatory Characteristics

Pericoronary adipose tissue CT attenuation was significantly higher around culprit lesions compared with nonculprit lesions within patients with ACS and the highest-grade stenosis lesions of controls (eFigure 2 in the Supplement). Within all 74 lesions of the ACS group, PCAT CT attenuation had a receiver operating characteristic area under the curve of 0.70 (95% CI, 0.55-0.83) to identify culprit lesions, with the highest Youden index at the attenuation cutoff of −68.2 HU and an accuracy of 73%. The frequency of increased PCAT CT attenuation (≥−68.2 HU) was higher in culprit lesions compared with nonculprit lesions within patients with ACS (47.8% vs 15.7%; P = .003). Pericoronary adipose tissue CT attenuation of all 74 lesions within ACS group correlated more strongly with intermediate-attenuation NCP (r = 0.393, P = .001) over low-attenuation NCP (r = 0.221, P = .06), high-attenuation NCP burden (r = −0.103, P = .38), and CP burden (r = 0.012, P = .92). In a multivariable logistic regression analysis, low-attenuation NCP burden (increase of 1%; odds ratio [OR], 1.43; 95% CI, 1.2-1.8; P < .001), intermediate-attenuation NCP burden (increase of 1%; OR, 1.27; 95% CI, 1.0-1.4; P < .01), and PCAT CT attenuation (1 HU increase; OR, 1.2; 95% CI, 1.0-1.3; P = .01) were related to the presence of culprit lesions.

Discussion

In our study, PCAT CT attenuation, a novel surrogate marker of coronary inflammation, was increased around culprit lesions compared with nonculprit lesions in patients with ACS and significantly stenosed lesions in stable CAD controls. Our results suggest that this imaging biomarker may help to identify vulnerable plaques.

Our findings of increased low- and intermediate-attenuation NCP burden in culprit lesions compared with non-culprit lesions within the same patients suggest that these high-risk anatomical features can be identified noninvasively, which is consistent with prior studies.8-11 In intravascular ultrasonography studies, fibrofatty tissue/intermediate-attenuation NCP and the necrotic core/low-attenuation NCP demonstrated lipid components, whereas fibrous tissue/high-attenuation NCP contained densely packed collagen.12 Previous research using an invasive angiographic identification of culprit lesions also described a higher frequency of low-attenuation lesions (<30 HU) in patients with ACS than in stable CAD.9,11

Antonopoulos et al6 also described elevated PCAT CT attenuation around stented culprit lesions after myocardial infarction (n = 10) compared with stented, nonculprit lesions in patients with stable CAD (n = 11).6 In contrast to our study, CTA was performed after percutaneous coronary intervention, the control group was not matched, and each culprit lesion was already stented when CTA was performed. It is unknown whether stented lesions are comparable with unstented lesions regarding the PCAT quantification because the percutaneous coronary intervention itself may affect the coronary inflammatory state and stent artifacts may hamper the accuracy of PCAT quantification. To our knowledge, ours is the first study to measure PCAT CT attenuation per lesion and systematically compare preintervention PCAT CT attenuation for culprit lesions with the highest-grade stenosed lesions in matched patients with stable CAD.

To our knowledge, this is the first report of a higher frequency of increased PCAT CT attenuation (≥−68.2 HU) for culprit lesions compared with nonculprit lesions in ACS and the first attempt to provide potential cutoffs to distinguish between culprit and nonculprit lesions by CTA. Lu et al13 reported that participants with high-risk plaques had lower epicardial adipose tissue CT attenuation compared with participants without high-risk plaques in noncontrast coronary calcium CT. By measuring PCAT thickness instead of PCAT CT attenuation in 103 patients undergoing CTA, catheterization, and intravascular ultrasonography, Okubo et al14 showed that PCAT thickness instead of epicardial adipose tissue thickness was independently associated with vulnerable plaque features.

To our knowledge, this is the first report directly investigating the association between PCAT CT attenuation and NCP components of low- and intermediate-attenuation NCP burden in culprit lesions. The correlation with intermediate-attenuation NCP in the underlying coronary segments provides a biological plausibility that PCAT CT attenuation describes biological processes that are associated with high-risk plaque characteristics instead of local vascular calcium deposition.6

The association between PCAT CT attenuation and high-risk plaque characteristics may reflect vascular inflammation causing morphological changes of PCAT and may influence plaque stability.6 Quantitative PCAT CT attenuation does not require extra protocols within routine CTA and may represent a dynamic imaging biomarker of vascular inflammation, enabling a simple, noninvasive identification of both coronary inflammation and vulnerable plaques from routine CTA.

Limitations

This is a single-center and single-vendor study investigating a small, predominantly male population with CAD. As PCAT shares the blood supply with the coronary arteries, PCAT enhancement might be related to contrast media–induced lumen enhancement. Furthermore, the influence of different image acquisition parameters and different CT scanners on PCAT quantification should be investigated in future studies. Finally, our study lacked a second external cohort for validating PCAT attenuation thresholds.

Conclusions

Pericoronary adipose tissue CT attenuation, a novel surrogate biomarker of coronary inflammation derived from routine CTA, in combination with high-risk plaque features, can potentially identify vulnerable plaques and may be a valuable tool to guide future prevention strategies.

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Article Information

Accepted for Publication: May 22, 2018.

Corresponding Author: Damini Dey, PhD, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, S. Mark Taper Bldg, Los Angeles, CA 90048 (damini.dey@cshs.org).

Published Online: July 18, 2018. doi:10.1001/jamacardio.2018.1997

Author Contributions: Drs Dey and Goeller had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Goeller, Achenbach, Berman, Dey.

Acquisition, analysis, or interpretation of data: Goeller, Cadet, Kwan, Commandeur, Slomka, Gransar, Albrecht, Tamarappoo, Berman, Marwan, Dey.

Drafting of the manuscript: Goeller, Cadet, Kwan, Albrecht, Dey.

Critical revision of the manuscript for important intellectual content: Goeller, Achenbach, Kwan, Commandeur, Slomka, Gransar, Albrecht, Tamarappoo, Berman, Marwan, Dey.

Statistical analysis: Goeller, Gransar, Albrecht, Dey.

Obtained funding: Dey, Achenbach.

Administrative, technical, or material support: Goeller, Achenbach, Kwan, Slomka.

Supervision: Goeller, Achenbach, Kwan, Albrecht, Marwan, Dey.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Drs Slomka, Berman, and Dey received software royalties from Cedars-Sinai Medical Center and have a patent. Mr Cadet received software royalties from Cedars-Sinai Medical Center. No other disclosures are reported.

Funding/Support: This study was funded by National Institute of Health/National Heart, Lung, and Blood Institute grant 1R01HL133616 (Dr Dey) and also by Bundesministerium für Bildung und Forschung (01EX1012B, Spitzencluster Medical Valley) and the Cardiac Imaging Research Initiative (Adelson Medical Research Foundation).

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
1.
Ross  R.  Atherosclerosis—an inflammatory disease.  N Engl J Med. 1999;340(2):115-126. doi:10.1056/NEJM199901143400207PubMedGoogle ScholarCrossref
2.
Fishbein  MC, Siegel  RJ.  How big are coronary atherosclerotic plaques that rupture?  Circulation. 1996;94(10):2662-2666. doi:10.1161/01.CIR.94.10.2662PubMedGoogle ScholarCrossref
3.
Fleg  JL, Stone  GW, Fayad  ZA,  et al.  Detection of high-risk atherosclerotic plaque: report of the NHLBI Working Group on current status and future directions.  JACC Cardiovasc Imaging. 2012;5(9):941-955. doi:10.1016/j.jcmg.2012.07.007PubMedGoogle ScholarCrossref
4.
Cheng  VY, Slomka  PJ, Le Meunier  L,  et al.  Coronary arterial 18F-FDG uptake by fusion of PET and coronary CT angiography at sites of percutaneous stenting for acute myocardial infarction and stable coronary artery disease.  J Nucl Med. 2012;53(4):575-583. doi:10.2967/jnumed.111.097550PubMedGoogle ScholarCrossref
5.
Dweck  MR, Fayad  ZA.  Imaging: perivascular fat—an unheralded informant of coronary inflammation.  Nat Rev Cardiol. 2017;14(10):573-574. doi:10.1038/nrcardio.2017.127PubMedGoogle ScholarCrossref
6.
Antonopoulos  AS, Sanna  F, Sabharwal  N,  et al.  Detecting human coronary inflammation by imaging perivascular fat.  Sci Transl Med. 2017;9(398):eaal2658. doi:10.1126/scitranslmed.aal2658PubMedGoogle ScholarCrossref
7.
Dey  D, Achenbach  S, Schuhbaeck  A,  et al.  Comparison of quantitative atherosclerotic plaque burden from coronary CT angiography in patients with first acute coronary syndrome and stable coronary artery disease.  J Cardiovasc Comput Tomogr. 2014;8(5):368-374. doi:10.1016/j.jcct.2014.07.007PubMedGoogle ScholarCrossref
8.
Hell  MM, Motwani  M, Otaki  Y,  et al.  Quantitative global plaque characteristics from coronary computed tomography angiography for the prediction of future cardiac mortality during long-term follow-up.  Eur Heart J Cardiovas Imaging. 2017;18(12):1331-1339. doi:10.1093/ehjci/jex183PubMedGoogle ScholarCrossref
9.
Motoyama  S, Ito  H, Sarai  M,  et al.  Plaque characterization by coronary computed tomography angiography and the likelihood of acute coronary events in mid-term follow-up.  J Am Coll Cardiol. 2015;66(4):337-346. doi:10.1016/j.jacc.2015.05.069PubMedGoogle ScholarCrossref
10.
Pflederer  T, Marwan  M, Schepis  T,  et al.  Characterization of culprit lesions in acute coronary syndromes using coronary dual-source CT angiography.  Atherosclerosis. 2010;211(2):437-444. doi:10.1016/j.atherosclerosis.2010.02.001PubMedGoogle ScholarCrossref
11.
Puchner  SB, Liu  T, Mayrhofer  T,  et al.  High-risk plaque detected on coronary CT angiography predicts acute coronary syndromes independent of significant stenosis in acute chest pain: results from the ROMICAT-II trial.  J Am Coll Cardiol. 2014;64(7):684-692. doi:10.1016/j.jacc.2014.05.039PubMedGoogle ScholarCrossref
12.
Nair  A, Kuban  BD, Tuzcu  EM, Schoenhagen  P, Nissen  SE, Vince  DG.  Coronary plaque classification with intravascular ultrasound radiofrequency data analysis.  Circulation. 2002;106(17):2200-2206. doi:10.1161/01.CIR.0000035654.18341.5EPubMedGoogle ScholarCrossref
13.
Lu  MT, Park  J, Ghemigian  K,  et al.  Epicardial and paracardial adipose tissue volume and attenuation—Association with high-risk coronary plaque on computed tomographic angiography in the ROMICAT II trial.  Atherosclerosis. 2016;251:47-54. doi:10.1016/j.atherosclerosis.2016.05.033PubMedGoogle ScholarCrossref
14.
Okubo  R, Nakanishi  R, Toda  M,  et al.  Pericoronary adipose tissue ratio is a stronger associated factor of plaque vulnerability than epicardial adipose tissue on coronary computed tomography angiography.  Heart Vessels. 2017;32(7):813-822. doi:10.1007/s00380-017-0943-1PubMedGoogle ScholarCrossref
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