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Cardiac Computed Tomography

Assessment of Coronary Inflammation and Other Plaque Features
Originally publishedhttps://doi.org/10.1161/ATVBAHA.119.312899Arteriosclerosis, Thrombosis, and Vascular Biology. 2019;39:2207–2219

Abstract

Unstable coronary plaques that are prone to erosion and rupture are the major cause of acute coronary syndromes. Our expanding understanding of the biological mechanisms of coronary atherosclerosis and rapid technological advances in the field of medical imaging has established cardiac computed tomography as a first-line diagnostic test in the assessment of suspected coronary artery disease, and as a powerful method of detecting the vulnerable plaque and patient. Cardiac computed tomography can provide a noninvasive, yet comprehensive, qualitative and quantitative assessment of coronary plaque burden, detect distinct high-risk morphological plaque features, assess the hemodynamic significance of coronary lesions and quantify the coronary inflammatory burden by tracking the effects of arterial inflammation on the composition of the adjacent perivascular fat. Furthermore, advances in machine learning, computational fluid dynamic modeling, and the development of targeted contrast agents continue to expand the capabilities of cardiac computed tomography imaging. In our Review, we discuss the current role of cardiac computed tomography in the assessment of coronary atherosclerosis, highlighting its dual function as a clinical and research tool that provides a wealth of structural and functional information, with far-reaching diagnostic and prognostic implications.

Highlights

  • Coronary computed tomography angiography (CCTA) is a first-line diagnostic test in the investigation of suspected coronary artery disease. CCTA provides a personalized assessment of the future cardiac risk, based on patient-specific plaque phenotyping and is often used to guide the deployment of appropriate prevention and medical management strategies.

  • Traditional usage of CCTA includes the quantitative assessment of coronary artery luminal stenosis, coronary artery calcification, and high-risk plaque features, including qualitative features such as low-attenuation plaque, napkin-ring sign, and spotty calcification.

  • Recent innovative approaches to CCTA have provided the first validated noninvasive biomarker of perivascular inflammation in the form of the fat attenuation index, which provides incremental prognostic value for cardiac mortality beyond all traditional risk factors and current CCTA interpretation approaches.

  • The rapid progress of artificial intelligence applications to medical imaging is now allowing innovative applications through the analysis of radiomic data to better predict coronary artery disease risk, select patients for treatments, and monitor such treatments.

Acute coronary syndromes (ACS) are often the first manifestation of coronary artery disease (CAD) and are mainly caused by the rupture or erosion of unstable coronary atherosclerotic plaques.1,2 Detecting such high-risk lesions at the earliest stages of CAD would facilitate timely medical interventions to stymie the progression of coronary atherosclerosis and prevent often catastrophic complications. In the era of precision medicine, there is an imperative need to move away from population-based risk factors and models, and towards an individualized assessment of cardiovascular biology and risk. Noninvasive imaging modalities aim to address this need but to ensure a meaningful impact on healthcare and patient outcomes, such methods need to be widely available, safe, accurate, and ultimately cost-effective.3

Please see www.ahajournals.org/atvb/atvb-focus for all articles published in this series.

Cardiac computed tomography (CT) and more specifically coronary CT angiography (CCTA) is a first-line diagnostic test in the investigation of suspected CAD.4–6 As opposed to invasive coronary angiography or functional testing that detects luminal narrowing and myocardial ischemia, CCTA provides a unique platform to assess the coronary lumen, plaque if present,7 and the structure of the cardiac chambers and myocardium.8 As such, CCTA provides a more personalized assessment of the future cardiac risk, based on patient-specific plaque phenotyping and is often used to guide the deployment of appropriate prevention and medical management strategies.9,10

In this Review, we discuss the current role of cardiac CT in detecting and characterizing coronary atherosclerosis and its use as a first-line diagnostic test in the assessment of CAD. We discuss how our expanding understanding of the pathophysiology of CAD and the rapid technological advances in CT enable a comprehensive anatomic and functional assessment of the coronary vasculature. We also review recent paradigm shifts in the noninvasive imaging of coronary atherosclerosis, moving away from the simple analysis of luminal stenosis to a more detailed characterization of the coronary wall and perivascular region, with important diagnostic and prognostic implications for noninvasive cardiovascular imaging.

CCTA: Current Evidence and Clinical Guidelines

Traditionally, clinical CCTA has been used to exclude the presence of anatomically significant CAD with an estimated sensitivity of 95% to 99% and specificity of 64% to 83%, and as such it is reserved as the diagnostic tool of choice in patients with a low-to-intermediate pretest probability (15%–50%) of CAD.6 The clinical value of CCTA is supported by the results of2 landmark trials, namely PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) and SCOT-HEART (Scottish Computed Tomography of the Heart), which explored the role of CCTA in the diagnostic assessment of patients with suspected stable CAD.9,10 In PROMISE, a diagnostic pathway that included initial anatomic assessment with CCTA was associated with fewer catheterizations showing nonobstructive CAD, compared with a strategy involving functional testing.10 Similarly, in SCOT-HEART, CCTA in addition to standard care in patients with angina secondary to suspected CAD was associated with a significantly higher certainty of diagnosis of angina due to coronary heart disease at 6 weeks, compared with standard care alone and improved subsequent targeting of diagnostic and medical interventions.9 Notably, while PROMISE did not demonstrate any beneficial effects of CCTA for a composite end point of death, myocardial infarction, hospitalization for unstable angina, or major procedural complication over a median follow-up interval of 25 months, the more recent 5-year follow-up data from SCOT-HEART showed a significant reduction in the incidence of myocardial infarction among patients randomized to CCTA compared with standard care.11 Furthermore, in the ROMICAT-II study (Rule Out Myocardial Infarction/Ischemia Using Computer Assisted Tomography), in an emergency department setting of patients with suspected ACS but without changes suggestive of ischemia on ECG or an initial positive troponin test, CCTA shortened the length of in-hospital stay and led to more direct discharges from the emergency department at the cost of an increase in downstream testing and radiation exposure, compared with standard evaluation.12 These trials have been instrumental in establishing CCTA as a first-line diagnostic tool in both patients with suspected stable CAD,6 as well as selected cases with suspected ACS.4

Quantitative Assessment of Coronary Atherosclerosis

Extent and Severity of Luminal Stenosis

Despite high-quality evidence on the clinical value of CCTA compared with other diagnostic modalities in the assessment of CAD, the clinical interpretation of CCTA scans has remained relatively unchanged over the years, focusing on the degree of luminal obstruction using widely acceptable thresholds (ie, minimal: <25% stenosis; mild: 25%–49% stenosis; moderate: 50%–69%; and severe: 70%–99%; occluded).13 While clinical decision pathways often rely on the rather simplified dichotomous characterization of CAD as nonobstructive or obstructive,14 it is known that not only the anatomic severity but also the extent of CAD are critical determinants of future cardiovascular risk. In a single-center consecutive cohort of 1127 patients with chest symptoms, coronary plaque severity, global coronary artery plaque extent, coronary artery plaque distribution, presence of left main or left anterior descending artery plaque, and 3-vessel coronary artery plaque were all demonstrated to be linked to future all-cause mortality risk.15 Similarly, in a test sample of 17 793 patients and a validation set of 2506 patients with suspected CAD, the best-performing predictors of all-cause mortality were the number of proximal segments with mixed or calcified plaques and the number of proximal segments with a stenosis >50%. These findings confirmed that both plaque burden and stenosis, particularly in proximal segments, carry incremental prognostic value for future cardiovascular risk prediction.16 Since the majority of clinical CCTA scans show no evidence of obstructive CAD,9,10 while a significant percentage of ACS occur in patients with nonobstructive disease, improving cardiovascular risk prediction requires a more comprehensive, individualized assessment of coronary atherosclerosis, and patient-specific vascular biology. Modern CCTA attempts to address this need by enabling a comprehensive assessment of coronary atherosclerosis through a range of luminal, vascular, and perivascular quantitative and qualitative features (Table 1).

Table. Summary of CT-Derived Luminal, Vascular, and Perivascular Features for Characterization of Coronary Atherosclerosis

CT Biomarker/IndexDescription/DefinitionBiological MeaningLimitations
Luminal
 Degree of luminal stenosisUsually, percent (%) diameter stenosis compared with a reference proximal/distal healthy segment. When measured in each coronary segment and vessel can be used to generate combined risk scores that describe the severity and extent of CAD (eg, segment stenosis score, segment involvement score, modified Duke Prognostic CAD index, and CAD-RADS classification system).An anatomic index reflecting the degree of luminal stenosis caused by a coronary plaque.Sensitive to technical artifacts that may lead to over- or under-estimation of the degree of coronary stenosis (eg, poor opacification, blooming/beam hardening for coronary calcium).
 Mean lesion area/diameterMinimum luminal diameter/area over a given lesion.An anatomic index reflecting the degree of luminal stenosis caused by a coronary plaque.Same as with degree of luminal stenosis.
Vascular
 Coronary artery calcium scoreDescribes the extent of coronary calcification, usually on noncontrast, CT images. Commonly calculated using the Agatston formula that accounts for both the volume and density of a given calcified lesion.An established cardiovascular risk score that provides an easy, indirect assessment of the extent of coronary atherosclerosis. Of potential value in primary prevention.Questionable value in secondary prevention, calcium does not regress in response to risk reduction measures, such as statins.
 Total plaque burdenTotal plaque volume/vessel volume (%).A quantitative volumetric index of total plaque burden.Same as with degree of luminal stenosis.
 Noncalcified/calcified/low-attenuation plaque burdenTotal noncalcified/calcified/low-attenuation plaque volume/vessel volume (%).A quantitative volumetric index of plaque burden and composition.Attenuation maps are used as surrogate markers of histological composition—often not validated across different scanners and parameters.
High-risk plaque features
 Low-attenuation plaque≥3 regions of interest (0.5–1 mm2) in the plaque with a mean attenuation of <30 HU.A large, lipid-rich, necrotic core within a plaque, reflecting an extracellular mass in the intima induced by necrosis and apoptosis of lipid-laden macrophage foam cells.Operator-dependent sign.
 Spotty calcificationCalcified plaque with <3 mm diameter, length <1.5 times the vessel diameter, and width of the calcification less than two-thirds of the vessel diameter.Sites of early vascular calcification.Operator-dependent, angle-dependent measurement.
 Napkin-ring signA ring-like peripheral higher attenuation of the noncalcified portion of the coronary plaque.A high-risk plaque phenotype composed of a thin fibrotic cap above a necrotic core.Subjective, operator-dependent, and qualitative sign.
 Positive remodelingA remodeling index >1.1 (ratio of maximum vessel area (or diameter)/proximal vessel area (or diameter)).A plaque-specific index reflecting positive outwards remodeling, possibly secondary to extracellular matrix remodeling (eg, increased MMP activity, and cell migration).Operator-dependent, angle-dependent measurement.
Hemodynamic
 Contrast density differenceMaximum difference in contrast density over lesion with respect to proximal site.Hemodynamic impairment to blood flow may be associated with a rapid change in contrast density along a given lesion.Several technical confounders that may affect attenuation.
 FFRCTA noninvasive approximation of the invasive fractional flow reserve of a given lesion using the anatomic structure of the coronary vasculature and advanced computational fluid dynamic modeling.Estimates the likelihood that a given stenosis impedes oxygen delivery to the myocardium.Expensive, requires third-party software.
Perivascular
 FAIThe weighted average attenuation of the perivascular adipose tissue within a radial distance from the outer vessel wall equal to the diameter of the adjacent vessel.A quantitative metric of coronary inflammation that tracks the vessel-induced effects on perivascular fat composition.Requires expert operators and access to specialized software to perform the analysis.

CAD indicates coronary artery disease; CAD-RADS, Coronary Artery Disease-Reporting and Data System; CT, computed tomography; FAI, fat attenuation index; FFRCT, fractional flow reserve—computed tomography; and MMP, matrix metalloproteinase.

Coronary Calcium

Further to the quantitative assessment of luminal stenosis, cardiac CT enables the noninvasive histological characterization of coronary anatomy through attenuation-based analysis of vascular wall composition.17 One of the first and most extensively studied methods is the assessment of coronary artery calcification by means of the Agatston coronary artery calcium (CAC) score. Traditionally obtained through ECG-gated noncontrast CT scans of the heart, CAC provides a simple and quick indirect assessment of the extent of coronary atherosclerosis by quantifying the volume and relative density of high-attenuation voxels in the heart, which correspond to sites of vascular calcification.18 It is known that with age CAC will slowly increase in both sexes and that this increase follows predictable progression curves—hence not all CAC is pathological and the test is nonspecific for this reason.19,20 The key focus of CAC scoring tools has been on differentiating between slow age-related CAC progression and rapid progression of CAC, which predisposes patients to increased risk. Both absolute CAC and a relative increase in CAC over time have been shown to predict incident CAD and adverse cardiac events independently of traditional cardiovascular risk factors.18,21

In asymptomatic elderly patients, CAC has been proven to be a useful tool to reclassify persons into more appropriate risk categories beyond Framingham risk score.22 To this end, risk scores such as the MESA (Multi-Ethnic Study of Atherosclerosis) risk score have been developed to reflect the additional benefit of CAC in risk prediction.23 It has recently been shown that CAC progression may be of weak importance in risk prediction and that the plaque load of the latest CAC score for patients with repeat scans provides the best predictor of risk.24 There is also much inquiry into the clinical implications of a CAC of zero. In cohorts of patients with both acute25 and stable chest pain,26 a zero calcium score has been demonstrated to be rarely associated with obstructive CAD and to confer excellent prognosis. This has led to the conclusion that CAC can serve as a reliable gate-keeper to prevent further unnecessary invasive examinations such as CCTA or invasive coronary angiography. However, the populations of these studies need to be considered as there is also evidence from large cohorts such as CORE64 (Coronary Artery Evaluation Using 64-Row Multidetector CT Angiography)27 and the CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry)28 registry that a zero calcium score cannot always be a reliable rule-out tool, particularly in patients with chest symptoms or a higher pretest probability of CAD. Other limitations of CAC that are important to note include that it has little clinical utility within the secondary prevention population,29 and that statin therapy has recently been shown to be associated with an increase prevalence of coronary plaques possessing calcium despite the risk of adverse cardiovascular events declining.30

A recent important finding from the SCOT-HEART trial was that among the traditional measures reported in clinical CCTA the predominant measure governing patient outcomes was the overall burden of atherosclerosis as calculated via CAC.31 CAC was the only independent predictor of risk within this large randomized controlled trial of cardiac CT in outpatients suspected of angina pectoris, with plaque composition and plaque hemodynamic consequences only being associated with future myocardial infarction. Currently, CAC is recommended in the risk stratification of selected cases where a risk-based treatment approach remains uncertain based on traditional risk factors.5 Importantly, novel measures of CAD risk such as perivascular fat attenuation index (FAI)32 and perivascular fat radiomic profile33 avoid many of these limitations and provide a dynamic measure of disease risk that, in the case of FAI, is responsive to therapeutic intervention34 and not associated with aging.35 This is achieved by linking atherogenic disease processes such as inflammation, fibrosis, and angiogenesis with radiomic features extracted from CCTA images, and is discussed at length below. In summary, CAC remains an inflexible biomarker that does not regress, and may even increase in response to risk reduction interventions such as statins,36 thus limiting its value in secondary prevention.

Identifying the High-Risk Plaque

Coronary calcification may represent a healing process of atherosclerotic plaques, leading towards a more stable phenotype.36 While CAC may provide an indirect assessment of the extent of coronary atherosclerosis in patients without an established diagnosis of CAD, more detailed CT-based characterization of coronary plaques can provide a deeper insight into the underlying biological processes that trigger atherogenesis and plaque instability (Figure 1).37,38 On CT, vascular tissue radiodensity is a good surrogate marker of histological composition (as assessed on intravascular ultrasound—virtual histology), with calcified plaques corresponding to higher attenuation values (eg, >465 Hounsfield Units [HU]), compared with fibrotic (eg, 65–260 HU) and low-attenuation lesions with a necrotic core (eg, −1 to 64 HU).17 The application of such HU maps enables the semi-automated segmentation and characterization of plaque composition and structure on dedicated analysis platforms. Such measurements may be quantitative (ie, total/calcified/noncalcified plaque burden, diameter stenosis, remodeling index) or qualitative (ie, low-attenuation plaque, napkin-ring sign, and spotty calcification; Table 1).37 For instance, while CT lacks the spatial resolution to detect thin cap fibroatheroma, a hallmark of unstable coronary plaques, CT-derived high-risk plaque features such as positive remodeling (a relative increase in the cross-sectional diameter of a given lesion compared with a proximal, reference segment of 1.1 or higher) and low-attenuation plaque (traditionally defined as a plaque area with mean attenuation <30 HU) can discriminate thin cap fibroatheroma from non- thin cap fibroatheroma lesions,39 thus identifying unstable coronary lesions that may lead to ACS.40 Spotty calcification and napkin-ring sign (ring-like peripheral higher attenuation of the noncalcified portion of the coronary plaque) have also been recognized as important high-risk plaque features.41 These high-risk features are key components of the natural history of atherosclerosis, especially among high-risk populations such as patients with diabetes mellitus,42 and have also been found to independently predict the presence of ACS in patients presenting with acute chest pain,38 as well as the future incidence of adverse cardiac events.31 More importantly, they appear to describe a dynamic biology and have been shown to regress in response to statin treatment,43 or more targeted anti-inflammatory biologic therapies.44

Figure.

Figure. Comprehensive computed tomography (CT)-based assessment of coronary atherosclerosis. Coronary CT angiography (CCTA) offers multiple ways of assessing the anatomic, functional, and biological characteristics of coronary atherosclerosis at different disease stages. Traditionally, CCTA has been used to identify anatomically significant coronary lesions and quantify the degree of luminal stenosis caused by their presence. However, with the use of CT-contrast agents, molecular CT and fusion positron emission tomography/CT with clinical or experimental radiotracers also facilitate the in vivo visualization of the earliest pathophysiological processes leading to atherosclerosis, such as inflammation, apoptosis and cell migration (A). Furthermore, the recently described perivascular fat attenuation index (FAI;B) has shown that early coronary inflammation may be detected at an early stage by identifying spatial changes in perivascular fat attenuation. In the presence of coronary atherosclerosis, CCTA enables an accurate volumetric assessment and the break-down of plaque composition into a noncalcified, calcified and low-attenuation component, reflecting distinct histopathologic entities with differing risk profiles (C). Distinct high-risk plaque features, such as spotty calcification, positive remodeling (D), and low-attenuation plaque (E) are further associated with an unstable plaque phenotype independently of the degree of luminal stenosis. In addition, computational fluid dynamic modeling (F) enables a noninvasive assessment of the hemodynamic (ischemia-causing) effects of a coronary lesion independently of its degree of anatomic stenosis. Finally, dual-energy and spectral CT (G) may further improve the histopathologic characterization of plaque composition by analyzing the differential attenuation of x-ray beams at different energy levels. FDG indicates fluorodeoxyglucose; FFRCT, Fractional flow reserve—computed tomography; and PVAT, perivascular adipose tissue.

These high-risk qualitative and quantitative features offer an individualized insight into the vascular biology of each plaque and patient, possibly explaining their incremental prognostic value beyond the degree of luminal stenosis.38 It is known that vascular remodeling in atherosclerosis may be attributed to cell migration and extracellular matrix remodeling through an imbalance in the relative expression and activity of matrix metalloproteinases and their inhibitors in the plaque microenvironment.45 The resulting outwards vascular remodeling can then be detected on CCTA as a relative increase in vascular diameter around the plaque.38,39 Low-attenuation plaque, on the contrary, reflects a lipid-rich necrotic core, an extracellular conglomerate within the intima induced by necrosis and apoptosis of lipid-laden macrophage foam cells.46,47 This high-risk plaque phenotype composed a thin fibrotic cap above a necrotic core is often described as a napkin-ring sign-on CCTA, translating to a low-attenuation area surrounded by a high-attenuation rim.7,38 Finally, spotty calcification identifies inflamed areas of confluent coronary calcification and microcalcification.37,38 Vascular calcification is viewed as a cellular response to a necrotic, inflammatory microenvironment, with a well-described association between inflammatory cell infiltration and osteoblastic metaplasia.48 Detecting areas of microcalcification at its earliest stages is a promising approach to identifying high-risk patients and lesions, but these do not become visible on CCTA until late in the natural history of atherosclerotic disease.49

It is important to note that recent work has demonstrated that the prevalence of high-risk plaque features such as spotty calcification, positive remodeling, and low-attenuation plaque is particularly low in the asymptomatic population,38 and that these feature are usually only present in up to one third of those who present with acute chest pain, with some features such as the napkin-ring sign reported to be present in as few at 1 in 20 patients with acute chest pain.37,38 High-risk plaque features also appear to have a low positive predictive value for future ACS. As already discussed above, an analysis of the recent SCOT-HEART trial demonstrated that the only independent predictor of cardiovascular risk was CAC, a surrogate measure of overall atherosclerotic plaque burden, with no individual high-risk features approaching the predictive power of the CAC.31 Despite this, for certain populations within the SCOT-HEART trial, high-risk plaque features did prove valuable to identify; for those with CAC <100 AU high-risk plaque features did confer a significantly elevated risk of coronary related death or nonfatal AMI as opposed to those with CAC >100 AU.31

Identifying the Hemodynamically Significant Plaque

The development of computational fluid dynamic modeling and machine learning now enables the functional assessment of the hemodynamic significance of coronary lesions through CCTA-derived estimation of the fractional flow reserve (FFR), that is, the ratio of the mean coronary pressure distal to a coronary stenosis to the mean aortic pressure during maximal coronary blood flow.50,51 CT-derived FFR (FFRCT)-guided therapy in patients with stable disease is associated with similar clinical outcomes but significantly lower costs in patients with stable CAD, compared with usual care.52 In addition, recent evidence suggests that FFRCT is effective in differentiating patients with intermediate-grade stenosis (30%–70%) who would benefit from invasive angiography (FFRCT <0.80) compared with no further management (FFRCT ≥0.80).53 However, both high-risk plaque features and FFRCT are only applicable in the presence of macroscopically visible atherosclerosis. Their ability to detect disease only at advanced stages limits their use to secondary prevention and highlights the need for noninvasive imaging modalities that can detect the key processes of early atherosclerosis (ie, inflammation, cell necrosis, or apoptosis) at the earliest stages.

Molecular CT Imaging to Detect Early Atherosclerosis

Cardiac CT has traditionally been used to provide an anatomic assessment of coronary atherosclerosis, and functional information on vascular biology was extrapolated through correlation with anatomic features (eg, inflammation and spotty calcium). Molecular imaging with positron emission tomography (PET) and selective radiotracers can supplement cardiac CT with a more direct assessment of vascular biology.54 PET-CT imaging with 18F-FDG (fluorodeoxyglucose), a radiotracer taken up by metabolically active inflammatory cells, has shown potential in identifying inflamed, vulnerable plaques after a strict protocol using a low-carbohydrate, high-fat preparation (to ensure suppression of myocardial uptake).55 More recently, novel radiotracers such as 18F-NaF (sodium fluoride) that are incorporated into hydroxyapatite formed in areas of microcalcification have shown promising diagnostic accuracy in identifying both culprit coronary lesions and abdominal aortic aneurysms with a high affinity for the vascular wall and minimal myocardial uptake.49,56,57 Clinically, 18F-FDG PET-CT is not used for risk stratification of coronary plaque. However, it has indications for the assessment of myocardial viability and blood flow, and for the detection and monitoring of other cardiovascular conditions such as myocarditis and sarcoidosis.58 Gallium-68-labeled dotatate is another tracer that binds to somatostatin receptor subtype-2, a receptor expressed exclusively in M1 pro-inflammatory macrophages. Similar to 18F-NaF,59 Ga-dotatate provides superior image quality compared with 18F-FDG and can identify culprit coronary and carotid lesions.60

Further to these clinically tested radiotracers, several agents and hybrid approaches have been tested in experimental settings. Polysaccharide-containing magnetofluorescent 20 nm nanoparticles labeled with61 Cu detect macrophages in atherosclerotic plaques of ApoE-lipoprotein knock-out mice through a trimodality imaging approach of PET, magnetic resonance, and optical detection.62 Other nanoplatforms detect chemokine receptors upregulated in pro-inflammatory macrophages from atherosclerotic plaques,63 and18F-labeled small vascular cell adhesion molecule type 1 affinity ligands allow identification of endothelial activation and inflammation.61 Similarly, SPECT/CT (single-photon emission computed tomography) imaging with 111In- and 123I-radiolabelled compounds targeting activated MMPs (matrix metalloproteinases) enables the in vivo detection of MMP activity and tracks the composition of atherosclerotic plaques both cross-sectionally as well as their response to treatment.64,65 Despite this, most of these approaches remain experimental, and their high costs, limited availability, high radiation exposure, and the paucity of clinical evidence demonstrating their incremental clinical utility in primary or secondary prevention66 has limited their translational potential. This area of CT imaging will likely continue to expand due to the adoption of PET-CT techniques by the pharmaceutical industry for the purposes of employing surrogate markers of atherosclerotic disease within early stage clinical trials.59,67,68

Perivascular Fat: A Window Into Vascular Biology

Vascular inflammation plays a causal role in the pathogenesis of atherosclerosis and plaque rupture69–72 and has been the primary focus of most experimental noninvasive imaging approaches.55,60–63 However, such approaches are often expensive, and not clinically available, whereas circulating inflammatory biomarkers may not be specific for vascular inflammation. Extracting this kind of information from traditional CCTAs would provide a cost-effective way of measuring coronary inflammation, using an existing and widely available imaging modality that is already part of clinical guidelines.32

Adipose tissue is a key regulator of cardiometabolic health.73,74 Perivascular adipose tissue (PVAT) forms a contiguous entity with the vascular adventitia and plays a key role in vascular homeostasis and atherogenesis by regulating the local microenvironment through the release of bioactive adipokines,75,76 gaseous and other lipid messengers.73,77 Higher inflammatory activity in PVAT, as assessed by 18F-FDG PET/CT has been implicated in a range of cardiovascular conditions, including stent-induced coronary vasoconstricting responses in pigs and vasospastic angina in humans.78,79 However, as we have recently demonstrated, the interactions between the vascular wall and PVAT are bidirectional.32,75,76,80 For instance, in the presence of increased vascular oxidative stress, lipid peroxidation products (eg, 4-hydroxynonenal) are generated and diffuse to the surrounding PVAT, activating PPAR-γ (peroxisome proliferator-activated receptor-γ) signaling in perivascular adipocytes and leading to increased production of adiponectin. Adiponectin is an antioxidant adipokine that may then diffuse back to the vascular wall and myocardial tissue and reduce superoxide production by inhibiting NADPH (nicotinamide adenine dinucleotide phosphate) oxidase activity.75,76 More recently, we demonstrated that in the presence of vascular inflammation, the release of pro-inflammatory mediators into the surrounding PVAT (eg, tumor necrosis factor-α, Interleukin-6, and interferon-γ) blocks the ability of perivascular preadipocytes to differentiate into mature lipid-laden adipocytes.32 In paired epicardial adipose tissue biopsies from a site attached to the right coronary artery and 2 cm away, we observed that perivascular adipocytes were significantly smaller and less well-differentiated (as evidenced by a lower relative expression of the adipocyte differentiation markers PPAR-γ and FABP4 [fatty acid-binding protein 4]) compared with those from the nonperivascular site.32 This vessel-induced gradient in PVAT composition reflects the inflammatory burden of a given coronary segment and has highlighted PVAT as a sensor of coronary inflammation, and therefore a potential host of diagnostic and prognostic biomarkers.32

Imaging Perivascular Fat

Cardiac CT offers the possibility of segmenting and analyzing adipose tissue depots through application of predefined validated HU cutoffs (−190 to −30 HU).32,81 CCTA, in particular, allows imaging of the vascular anatomy with the volumetric and qualitative characterization of PVAT along the coronary vessels. To translate our discoveries on the effects of coronary inflammation on PVAT composition, we recently described and validated a novel CT-derived biomarker, namely the perivascular FAI, which tracks spatial changes in PVAT composition induced by inflamed/diseased coronary vessels.32 Developed through a radiotranscriptomic approach that linked the CT phenotype of adipose tissue biopsies scanned ex vivo with their transcriptional profile, FAI relies on the concept that inflammation-induced changes in adipocyte size and lipid content are associated with a shift in CT attenuation towards a less negative HU range (towards −30 HU). The perivascular FAI (calculated by the CaRi [Cardiac Risk]-HEART algorithm developed by the University of Oxford) captures these gradients in the attenuation of the perivascular space, with high perivascular FAI linked to a higher inflammatory burden.32 The CaRi-HEART algorithm performs a number of adjustments related with technical CT scan characteristics, the local anatomy, the background adipocyte size, and others interpreted using AI modeling, and provides a robust quantitative method to quantify coronary inflammation and predict cardiac risk. Indeed, we have shown that CAD is associated with higher perivascular FAI compared with healthy individuals.32 In addition, perivascular FAI is significantly increased around culprit/unstable lesions in patients presenting with acute myocardial infarction.32 Notably, perivascular FAI exhibits dynamic changes around the culprit lesions, decreasing significantly when measured 5 weeks after the index event.32

Other groups have since presented results confirming our hypothesis and the basic principle behind FAI. PVAT attenuation (as it was first described by Antonopoulos et al32) has been found to be higher around atherosclerotic coronary segments compared with healthy segments, as assessed on intravascular ultrasound.82 Moreover, cases of perivascular fat stranding have been described around culprit lesions in patients with ACS or spontaneous coronary artery dissection.83 Although a qualitative finding, perivascular fat stranding may represent the late, macroscopic equivalent of the early, microscopic perivascular changes that are detected by FAI. In a recent study, Goeller et al84 independently confirmed that compared with nonculprit lesions, culprit lesions in patients with ACS are associated with significantly higher PVAT radiodensity. In further validation of the strong links between coronary inflammation and PVAT phenotype, higher perivascular fat radiodensity has been shown to strongly correlate with increased plaque inflammation as assessed by 18F-NaF uptake on PET-CT imaging,85 as well as the progression of total and noncalcified atherosclerotic plaque burden in the adjacent vessel.86 Of note, in symptomatic patients undergoing cardiac CT the information captured by the perivascular FAI is independent of coronary calcification (measured by CAC)32,77 or systemic markers of inflammation, such as hsCRP.77

The potential clinical value of pericoronary FAI mapping in improving cardiac risk prediction was recently explored in the CRISP-CT study,77 a multi-center study comprising 2 independent cohorts of 1872 and 2040 patients undergoing clinically indicated CCTA for stable chest pain investigation in 2 major centers in Europe and the USA with a median follow-up of 60 and 48 months, respectively.77 Higher perivascular FAI values around the proximal right coronary and left anterior descending arteries were linked to a higher adjusted risk of both all-cause and cardiac mortality.77 More importantly, FAI mapping offered incremental prognostic value for cardiac mortality beyond age, traditional risk factors, extent of CAD, and presence of high-risk plaque features in both cohorts. In post hoc analyses, a cutoff of −70.1 HU was ascertained and was associated with a 6- to 9-fold higher adjusted risk of future cardiac mortality in the 2 independent cohorts.77 Taken together, these findings confirmed that a tripartite approach, combining CT-derived features of the coronary lumen, vascular wall, and perivascular region can maximize the diagnostic and prognostic yield of CCTA, by providing complementary information on the disease status of a coronary vessel.

Perivascular FAI is yet to be tested in a clinical trial to assess the reversibility of coronary inflammation, as measured by FAI, in response to statin therapy, new anti-inflammatory agents (eg, canakinumab) or PCSK9 inhibitors.87 Despite this, there is good data to demonstrate that perivascular FAI is modifiable and may track the effects of anti-inflammatory interventions. In patients who received advice to commence treatment with statins or aspirin after CCTA, the perivascular FAI (measured before deployment of the new treatment) was no longer predictive of cardiac mortality (adjusted hazard ratio, 2.85; P=0.25). In contrast, among those who did not receive any recommendations for change of management after CCTA, the predictive value of high perivascular FAI values for cardiac mortality was retained (adjusted hazard ratio, 18.71; P=0.01).77 Notably, in a recent prospective cohort study of patients with moderate to severe psoriasis, anti-inflammatory treatment with novel biologics (anti-TNF [tumor necrosis factor]-a, anti-IL [interleukin]17, and anti-IL12/23) was associated with a significant reduction in perivascular FAI at one year, whereas no significant change was observed in individuals treated with topical or UV B phototherapy.34 This study is the best evidence to date that novel perivascular imaging techniques can be used to track response to interventions for coronary disease. Further, perivascular FAI measured around culprit lesions during ACS changes dynamically post-event, with significant changes being detectable as early as 5 weeks post-ACS and after the initiation of optimal secondary preventative therapies.32 It is important to note that through the use of radiomics, as discussed at length below, differing risk prediction signatures can be generated that identify differing aspects of atherogenesis within the PVAT which may or may not be altered by current treatments.33

CT as a Research Tool in Atherosclerosis

Given its noninvasive nature, strong diagnostic and prognostic value, high-spatial resolution and wide clinical adoption, cardiac CT is a major research tool in several observational and interventional clinical studies aiming to understand the effects of treatments, such as statins or anti-inflammatory interventions, on the progression of coronary atherosclerosis.43,88–90 Recent advances may further boost the use of CCTA by providing a range of functional biomarkers complementary to the traditional anatomic assessment of coronary plaques that track changes in coronary inflammation (Figure 1).32,77 In addition, micro-CT and micro-CT angiography techniques are increasingly integrated into experimental protocols and animal studies, supported by the development of targeted contrast agents and high-spatial resolution.91,92

The Future of CT-Based Plaque Analysis

Dual Energy, Spectral and High-Resolution CT

Technological developments in cardiac CT relate both to the acquisition of scans and the post-processing of the acquired images. Dual energy or spectral CT methods that use 2 or more photon energy levels, with or without contrast material such as atherosclerosis targeting59 Au-HDL nanoparticles, may improve the characterization of atherosclerotic plaque composition by studying tissue behavior at different X-ray energy spectra, although in an in vivo setting this may be limited by a decrease in image quality.93–96 New ultra-high resolution CT scanners (eg, U-HRCT, TSX-304R, Toshiba Medical Systems, Otawara, Japan) can generate images with higher than normal spatial resolution thanks to 0.25 mm×128-row detectors with 1792 channels and 1024×1024 matrix image reconstruction. Promisingly, studies to date using these ultra-high-resolution systems have shown excellent correlation with the degree of coronary artery stenosis quantified on invasive coronary angiography.97

Artificial Intelligence and Radiomics

The rapid progress in the field of artificial intelligence and machine learning may further boost the information yield of cardiac CT. As discussed in this review, cardiac CT analysis can provide a wide range of quantitative and qualitative features at the level of the patient, vessel, coronary segment, or individual plaque. Linear assumptions and traditional statistical approaches may be insufficient to capture this complexity.98 This was demonstrated in a pooled analysis of 10 030 patients with suspected CAD undergoing CCTA with 5-year follow-up, where a machine learning-based model including 25 demographic features and 44 CCTA-parameters outperformed traditional risk scores, with a C-statistic of 0.79 versus 0.61 for Framingham Risk Score, 0.64 for the segment stenosis score, 0.64 for the segment involvement score, and 0.62 for the modified Duke Index.99 In another study of 254 patients with FFR assessment of 484 vessels, a machine learning algorithm combining several lesion-specific features improved prediction of lesion-specific ischemia over and above any individual feature.100

It is important that one keeps in mind that CT scans are more than plain images; they are data. Treating CT scans as data rather than images is the focus of the field of radiomics, which uses mathematical formulae to compute hundreds of shape-, attenuation-, and texture-related features for a given anatomic volume or segmentation.98 Originally applied in the field of cancer imaging and tumor segmentation where radiomic features have been found to reliably discriminate malignant from benign lesions,101 radiomic approaches have recently been implemented in coronary analysis and CCTA.102 Qualitative features such as the napkin-ring sign that may be operator-dependent can now be detected through computation of radiomic features,102 paving the path for the development of automated scan interpretation and identification of novel CT-based biomarkers.103 Importantly, such features are also found in the perivascular region, such as the FAI that relies on radiomic phenotyping of PVAT composition to extract information about the inflammatory status of the adjacent vessel.32,77

The first study to apply radiomic techniques on CCTA scans for the purpose of better detection of CAD processes has recently been reported by our group.33 Radiomic signatures derived via a machine learning approach were able to detect coronary artery inflammation and features of radiomic texture were related to adipose tissue fibrosis and vascularity, as measured through gene expression in tissue samples obtained during cardiac surgery. In this study, we went on to apply an artificial intelligence derived algorithm named the fat radiomic profile into the SCOT-HEART trial where it significantly improved major adverse cardiac event prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic]=0.126; P<0.001). This represented an improvement in cardiac risk prediction beyond the current state of the art. Notably, we also reported that fat radiomic profile was unlike the other artificial intelligence derived marker of CAD risk, perivascular FAI, as the fat radiomic profile was not altered up to 6 months after an index cardiac event while FAI was shown to improve during this time, suggesting that fat radiomic profile detects PVAT changes beyond inflammation which are not captured by FAI and that are less susceptible to current therapeutics.

Current Limitations

Cardiac CT is not without limitations. Variations in clinical protocols and technical parameters during the acquisition, reconstruction, and analysis of CT images across centers may introduce bias in the analysis.104 Similarly, blurring and stairstep artifacts caused by motion and cardiac cycle phase misregistration, beam-hardening due to the presence of high-attenuation structures such as pacemaker leads or stents and associated blooming may affect image quality.104 Partial voluming further limits the spatial resolution of CT in discriminating neighboring structures of different attenuation values.43,104 However, ongoing technical improvements in newer generation scanners (eg, 320-slice CT scanners) now enable imaging of the whole heart in one cardiac cycle, thus limiting some of these artifacts, and may also lead to reductions in radiation doses. For the purposes of CCTA, clinical guidelines recommend using 64-slice CT scanners or higher.4,13,105 While there are no universally accepted guidelines in research, it is the view of the authors that technical acquisition and reconstruction settings should be standardized and prespecified in the protocol of each study to minimize any technical confounding and noise in image analysis. This is especially true for quantitative radiomic approaches that appear to be particularly sensitive to variations in tube voltage, slice thickness, and choice of reconstruction kernels.106 Finally, contrary to CAC scoring which is of particular value in asymptomatic patients at intermediate risk of CAD, most research studies using CCTA including those assessing perivascular attenuation mapping, have examined symptomatic patients undergoing clinically indicated CCTA and their validity in asymptomatic individuals remains unknown. Nevertheless, such methods do not aim to generate new indications for CCTA, but rather to maximize the diagnostic and prognostic yield of CCTA when performed according to current guidelines.

Conclusions

In recent years, cardiac CT has experienced a paradigm shift from the simple assessment of coronary luminal stenosis towards a more comprehensive quantitative assessment of coronary plaque composition and its functional (inflammatory and hemodynamic) status. Rapid advances in our understanding of the pathophysiological mechanisms of atherosclerosis and technological developments both in CT scanner manufacturing and image post-processing and analysis have revolutionized the field of cardiac CT and established CCTA as a first-line clinical and research imaging modality for the detailed phenotypic characterization of coronary atherosclerosis. Cardiac CT offers a unique toolkit that enables the combined analysis of luminal (eg, computation fluid dynamics), wall (eg, high-risk plaque features and plaque burden), and perivascular radiomic features (eg, perivascular FAI). This multi-dimensional approach enables a comprehensive assessment of coronary anatomy and biology with important implications and applications in both research and everyday clinical practice.

Nonstandard Abbreviations and Acronyms

ACS

acute coronary syndrome

CAC

coronary artery calcium

CAD

coronary artery disease

CCTA

coronary computed tomography angiography

CT

computed tomography

FABP4

fatty acid-binding protein 4

FAI

fat attenuation index

FDG

fluorodeoxyglucose

FFR

fractional flow reserve

FFRCT

fractional flow reserve—computed tomography

HU

Hounsfield units

PET-CT

positron emission tomography–computed tomography

PPAR-γ

peroxisome proliferator-activated receptor-γ

PVAT

perivascular adipose tissue

Footnotes

For Sources of Funding and Disclosures, see page 2216.

Correspondence to: Charalambos Antoniades, MD, PhD, Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom. Email

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