Assessing the Parameter—Vessel-to-Lesion-Specific Perivascular Fat Attenuation Ratio, as a New Imaging Marker of High Risk Plaques

Abstract

Background

The perivascular fat attenuation has emerged as a novel imaging biomarker of coronary artery inflammation in recent years. The present study assessed the role of lesion-specific perivascular fat attenuation (l-PVFA) and vessel-to-l-PVFA ratio (v/l- PVFAR) in plaque characterization and identifying the vulnerable/high risk plaques (HRPs) using computed tomography (CT) coronary angiogram.

Objectives

(1) To establish the significance of l-PVFA and v/l- PVFAR in the characterization of different types of plaques. (2) v/l PVFAR as a new imaging marker in the identification of HRPs.

Methods and Results

Thirty participants underwent CT coronary angiogram and we studied 70 diseased coronary arteries in these patients. Out of the 70 plaques studied retrospectively, 40 were calcified, 23 were partially calcified, and 7 were noncalcified plaques. l-PVFA and v/l PVFAR were calculated manually for different types of plaques and found significantly higher in noncalcified and partially calcified plaques, when compared with calcified plaques (p < 0.001). v/l PVFAR of > 2 is highly sensitive (100%) and specific (95.2%) for HRP with the diagnostic accuracy of 96.67%.

Conclusion

l-PVFA and v/l PVFAR highly correlate with various types of plaques. v/l- PVFAR is a useful biomarker in the identification of HRPs.

Keywords

CT coronary angiogram - high risk plaque - perivascular fat attenuation

Introduction

Computed tomography (CT) coronary angiography (CTCA) is a noninvasive and effective alternative to invasive catheter coronary angiography (CAG) for the assessment of coronary artery disease and stenosis grading.[1] The introduction of helical CT in the 1990s ushered in the era of CTCA. The late 1990s saw significant advancements in spatial and temporal resolution with the emergence of multislice helical CT scanners. Since the late 2000s, improvements in technology have contributed to the growing acceptance of CTCA as a noninvasive imaging option. The current trend in coronary artery imaging is shifting from simply assessing stenosis to evaluating plaque burden and its characteristics. In the future, CTCA may serve as a comprehensive noninvasive study for both anatomical and functional evaluations.[1] [2] [3] [4] [5]

In the early 2010s, CT scans were primarily recommended for patients deemed to have a low pretest risk for coronary artery disease (CAD) according to the National Institute for Health and Care Excellence (NICE) Clinical Guideline 95 (CG95). However, recent guidelines from the European Society of Cardiology,[6] NICE,[7] and the New American College of Cardiology/American Heart Association Chest Pain Guideline[8] now recommend CT as a first-line test (class I) for both acute and chronic coronary syndromes. Symptomatic patients with stable symptoms and an intermediate or high pretest probability of obstructive CAD, as well as those with intermediate-risk acute chest pain, are also considered for CTCA as the preferred imaging method. In 2016, the Coronary Artery Disease Reporting and Data System (CAD-RADS) was introduced as a standardized classification system for coronary artery stenosis, with CAD-RADS 2.0 published in 2022. This updated version incorporates assessments of plaque burden, ischemia evaluation, and redefines vulnerable plaque as high-risk plaque (HRP).[9] Plaque burden indicates the total amount of plaque in the coronary arteries, which is strongly linked to the incidence of coronary heart disease events and has significant prognostic value. CCTA's unique ability to detect both the presence and quantity of plaque burden distinguishes it from other invasive and noninvasive tests.[9]

Materials and Methods

Patient Selection

This retrospective study was conducted for a period of 1 year 6 months from March 2022 to August 2023 and was approved by Institutional Ethics Committee, Meenakshi Hospital, Tanjore, Tamil Nadu, India (ID No. ECR/946/Inst/TN/2017/RR-21). Thirty participants underwent CT coronary angiogram and we studied 70 diseased coronary arteries in these patients.

The different types of plaques were spotted and composition is differentiated by artificial intelligence (AI)-based Hounsfield units (HU). We also assessed lesion-specific and vessel-to-lesion-specific perivascular fat attenuation (v/l-PVFA) parameters. We also correlated v/l-PVFA ratio (v/l-PVFAR) for different types of plaques and their accuracy in detecting HRPs.

We have excluded patients with coronary artery bypass grafting, patients on immunosuppressant, pericardial effusion, contraindications for β-blockers (e.g., bronchial asthma), and signs of mediastinal inflammation.

Imaging Protocol and Analysis

All patients underwent scanning using a GE Revolution Maxima 128-slice CT scanner in the prospective electrocardiogram-gated sequential mode, with a tube voltage of 120 kV and a gantry rotation time of 0.35 seconds. Initially, a noncontrast CT angiography (CTA) was performed to assess the calcium score, captured at 75% of the R-R interval. For some patients, an intravenous β-blocker was administered slowly to achieve a target heart rate of < 70 beats per minute. Subsequently, a contrast-enhanced CTA was conducted with the intravenous administration of 60 to 90 mL of iodinated contrast medium (iopromide, Ultravist 370 mg I/mL, Bayer HealthCare), followed by a 20-mL saline chaser (0.9% NaCl). The injection flow rate was weight-based, set at 5 to 6 mL/s. Bolus tracking was used to initiate the coronary CTA acquisition, with a trigger threshold of 140 HU at 90 kV in the ascending aorta. Image reconstruction was performed using the advanced ADW 4.7 Workstation equipped with built-in AI processing capabilities.

The plaques are characterized based on HU with multiple parameters like calcification, soft tissue and necrotic/fat components, and with different color codes. The PVFA was calculated manually adjacent to the lesion (referred as l-PVFA with four values and mean average is calculated) ([Fig. 1]). Similarly, at the level of normal-appearing coronary artery (referred as vessel-specific PVFA with four values and mean average is calculated) ([Fig. 2]). The ratio is calculated between v/l-PVFAR) from mean average values.

Fig. 1 Lesion-specific perivascular fat attenuation (PVFA) with four values and mean average is calculated.

Fig. 2 Vessel-specific perivascular fat attenuation (PVFA) with four values and mean average is calculated.

Statistical Analysis

Statistical analyses were performed using SPSS version 26 and the Epi Info statistical package, version 7. Continuous variables were expressed as mean (standard deviation), while categorical variables were presented as counts (percentages). Associations between categorical variables were evaluated using the chi-square test and Fisher's exact test, as appropriate. One-way analysis of variance was employed to determine the significance of differences between groups. A p-value of < 0.05 was considered statistically significant.

Results

The Coronary Artery Disease Reporting and Data System (CAD-RAD) scoring among the 30 participants were: 5 (CAD-RADS 1), 7 (CAD-RADS 2), 10 (CAD-RADS 3), 6 (CAD-RADS 4), and 2 (CAD-RADS 5) ([Table 1]). Out of the 70 plaques studied retrospectively, 40 were calcified, 23 were partially calcified, and 7 were noncalcified plaques. Among them, 19 were diagnosed as HRPs ([Table 2]). l-PVFA and v/l-PVFAR were significantly higher in noncalcified and partially calcified plaques, when compared with calcified plaques (p < 0.001) ([Table 3]). l-PVFA and v/l-PVFAR were significantly higher in HRP (p < 0.001) ([Table 4]). v/l-PVFAR of ≥ 2 is highly sensitive (100%) and specific (95.2%) for HRP with the diagnostic accuracy of 96.67% ([Table 5]). Representative images are attached ([Figs. 3] [4] [5] [6] [7]).

Table 1 The CAD-RAD scoring among the 30 participants were: 5 (CAD-RADS 1), 7 (CAD-RADS 2), 10 (CAD-RADS 3), 6 (CAD-RADS 4), and 2 (CAD-RADS 5)

CAD-RADS score	Total
CAD-RADS 1	5 (16.6%)
CAD-RADS 2	7 (23.4%)
CAD-RADS 3	10 (33.4%)
CAD-RADS 4	6 (20%)
CAD-RADS 5	2 (6.6%)
Total	30 (100%)

Abbreviation: CAD-RADS, Coronary Artery Disease Reporting and Data System.

Table 2 Out of 70 plaques studied retrospectively, 40 were calcified, 23 were partially calcified, and 7 were noncalcified plaques

Plaque characterization	Calcified	Noncalcified	Partially calcified
Total	40	7	23

Table 3 Lesion-specific perivascular fat attenuation (l-PVFA) and vessel-to-lesion-specific perivascular fat attenuation ratio (v/l-PVFAR) were significantly higher in noncalcified and partially calcified plaques, when compared with calcified plaques (p < 0.001)

A
Variable	Calcified	Noncalcified	Partially calcified	p -Value
Lesion-specific PVFAI	−158.775 ± 21.788	−133.7142 ± 18.8389	−111.74 ± 52.841	0.001 Sig
B
Variable	Calcified	Noncalcified	Partially calcified	p -Value
Ratio	1.003190429 ± 0.03787	1.525834226 ± 0.22969	1.68184810 ± 0.55398	0.001 Sig

Table 4 Lesion-specific perivascular fat attenuation (l-PVFA) and vessel-to-lesion-specific perivascular fat attenuation ratio (v/l-PVFAR) were significantly higher in high-risk plaque when compared with nonhigh-risk plaque (p < 0.001)

A
Variable	Plaque characterization		p -Value
	Non-HRP ( n  = 51)	HRP ( n  = 19)
Vessel-specific PVFAI	−168.65 ± 25.12	−140.28 ± 34.75	0.001
B
Variable	Plaque characterization		p -Value
	Non-HRP ( n  = 51)	HRP ( n  = 19)
Lesion-specific PVFAI	−154.84 ± 21.61	−65.60 ± 19.18	0.001

Abbreviation: HRP, high-risk plaque.

Table 5 Vessel-to-lesion-specific perivascular fat attenuation ratio (v/l-PVFAR) of ≥ 2 is highly sensitive (100%) and specific (95.2%) for HRP with the diagnostic accuracy of 96.67%

A
Variable	Plaque characterization		p -Value
	Non-HRP ( n  = 51)	HRP ( n  = 19)
Ratio	1.11 ± 0.20	2.18 ± 0.32	0.001
B
Parameter	Estimate		Lower–Upper 95% CIs
Sensitivity	100%		(70.08, 100)
Specificity	95.24%		(77.33, 99.15)
Positive predictive value	90%		(59.58, 98.21)
Negative predictive value	100%		(83.89, 100)
Diagnostic accuracy	96.67%		(83.33, 99.41)

Abbreviations: CI, confidence interval; HRP, high-risk plaque.

Fig. 3 Representative images of multiple calcified plaques (A–H) with color (fatty [blue] < 30 Hounsfield units [HU], fibrous [pink] 30–400 HU, and calcified [yellow] > 400 HU) codes. (C, F, and I) showing lesion-specific perivascular fat attenuation (PVFA).

Fig. 4 Representative images of noncalcified plaque (A–D) with color (fatty [blue] < 30 Hounsfield units [HU], fibrous [pink] 30–400 HU, and calcified [yellow] > 400 HU) codes. (C and D) showing vessel- and lesion-specific perivascular fat attenuation (PVFA), respectively, without signs of high-risk plaque.

Fig. 5 Representative images of noncalcified plaque (A–F) with color (fatty [blue] < 30 Hounsfield units [HU], fibrous [pink] 30–400 HU, and calcified [yellow] > 400 HU) codes. (E and F) showing lesion- and vessel-specific perivascular fat attenuation (PVFA), respectively. In addition, (B) demonstrating necrotic core, while (C and D) demonstrating vascular remodeling favoring high-risk plaque.

Fig. 6 Representative images of noncalcified plaque (A–F) with color (fatty [blue] < 30 Hounsfield units [HU], fibrous [pink] 30–400 HU, and calcified [yellow] > 400 HU) codes. (E and F) showing lesion- and vessel-specific perivascular fat attenuation (PVFA), respectively. In addition, (B) demonstrating necrotic core, while (C and D) demonstrating vascular remodeling favoring high-risk plaque.

Fig. 7 Representative images of partially calcified plaque (A–F) with color (fatty [blue] < 30 Hounsfield units [HU], fibrous [pink] 30–400 HU, and calcified [yellow] > 400 HU) codes. (C and F) showing lesion- and vessel-specific perivascular fat attenuation (PVFA), respectively. In addition, (B) demonstrating spotty calcification, while (D and E) demonstrating vascular remodeling favoring high-risk plaque.

Discussion

CAD is prevalent in both developing and developed countries, resulting in high morbidity and mortality. While advancements in care and treatment have reduced morbidity associated with acute coronary syndrome (ACS), the incidence continues to rise, especially among patients with comorbidities such as diabetes and hypertension. ACS primarily results from the erosion and rupture of vulnerable plaques, leading to thrombosis.[10] The development of CAD begins with intimal thickening and can progress to fibrocalcific plaques. Thin fibrous cap atheromas are particularly prone to rupture and are often responsible for acute events.[11]

Histologically, HRPs are characterized by a large lipid core, a thin fibrous cap, and signs of active inflammation, such as macrophage or T cell infiltration.[10] The CTCA features that indicate HRP include: (1) spotty calcification—small, punctate calcifications measuring 1 to 3 mm; (2) low attenuation plaque—a noncalcified plaque with attenuation below +30 HU; (3) positive remodeling—the outer diameter of the vessel at the plaque site divided by the average outer diameter of the proximal and distal vessel must be greater than 1.1; and (4) napkin-ring sign—a high attenuation rim surrounding a low attenuation core. A plaque is classified as high risk in CTCA if it shows two or more of these characteristics.[9]

The focus on identifying coronary lesions and recognizing vulnerable plaques at risk for rupture began in 2001.[12] [13] Schroeder et al suggested that CTCA, as a noninvasive method, could become a vital tool for risk stratification in patients with known or suspected CAD.[13] Research in the early 2000s indicated that plaque characterization could be achieved through CT based on HU values, demonstrating diagnostic accuracy comparable to intravascular ultrasound (IVUS).[14] [15] Obaid et al developed CT-derived plaque maps for characterizing coronary artery plaques.[15] HRP characteristics linked to ACS include positive vascular remodeling, low plaque density, and spotty calcification, as noted by Motoyama et al.[16] Otsuka et al found that the napkin-ring sign on CTCA is strongly correlated with future ACS events.[17] Saremi et al examined the histopathologic classification of coronary atherosclerotic plaques, concluding that CT can effectively identify patients at higher risk for coronary events by detecting HRP.[18]

Bittner et al determined that the definitions of HRP feature in CTCA enable better assessments of ACS patients compared with those based on IVUS.[19] van Veelen et al highlighted the difficulty of assessing plaque characterization through catheter CAG and advocated for the development of noninvasive imaging modalities to enhance screening and guide prevention or treatment for CAD by effectively identifying vulnerable plaques.[10] Dawson et al indicated that advancements in coronary imaging may facilitate a shift in cardiovascular care by allowing early identification and management of HRPs.[20] Heinsen et al pointed out that CTCA not only assesses coronary artery stenosis but also evaluates plaque morphology, which is an important prognostic indicator. Plaques that display both low attenuation and positive remodeling (HRP) have a strong association with future cardiovascular events, even in asymptomatic individuals.[21]

Wang et al categorized coronary plaques (n = 32) into four groups based on IVUS plaque material properties. Similarly, our study classified plaques based on attenuation (HU) values.[22] Lee et al noted that the rapid advancement of AI technologies may soon support clinical applications like automated coronary artery calcium scoring and plaque analysis.[23] van Assen et al emphasized that AI could significantly enhance CAD evaluation, playing a critical role in identifying, quantifying, and prognosticating CAD. Furthermore, AI could be pivotal in the identification and clinical application of novel imaging biomarkers and optimizing workflows for cardiac CT and coronary plaque analysis.[24] The advent of AI makes the identification of the lipid necrotic core within HRP increasingly significant.

Epicardial adipose tissue, including perivascular fat surrounding coronary arteries, plays a crucial role in cardiovascular health by secreting proinflammatory adipokines. This proinflammatory environment is a known contributor to the pathogenesis of CAD and ACS. Additionally, inflamed coronary vessels can release cytokines that modify the function and properties of adjacent perivascular fat.[25]

The perivascular fat attenuation index (FAI) has been validated as a noninvasive marker of coronary inflammation. It is derived from standardized CTA images to detect changes in perivascular fat around major coronary arteries, including the right coronary artery, left anterior descending artery, and left circumflex artery. A perivascular FAI above −70.1 HU is predictive of increased all-cause and cardiac-related mortality, even after adjusting for age, sex, and other risk factors. Elevated perivascular FAI is also linked with reduced global coronary flow reserve in patients with stable CAD and has been documented in cases of spontaneous coronary artery dissection and vasospastic angina. Consequently, perivascular FAI is a potentially valuable tool for identifying at-risk ACS patients who do not exhibit significant coronary artery stenosis.[26]

There is an unmet need for biomarkers that noninvasively visualize coronary inflammation and track responses to anti-inflammatory interventions in atherosclerosis. Perivascular adipose tissue (PVAT) acts as an in vivo biosensor of coronary inflammation by modifying its composition in response to inflammatory signals. The FAI detects coronary inflammation by quantifying dynamic spatial changes in PVAT attenuation.[25] [26] The PVAT fat attenuation value is significantly higher in the region of HRP compared with calcified or stable plaques. The PVAT fat attenuation ratio is calculated as PVAT fat attenuation at the site of lesion (lesion-specific PVAT) and at the normal site of vessel. The ratio arbitrary value of > 2 is considered significant in differentiating HRP from stable plaques (p < 0.05).

While invasive CAG is the gold standard for evaluating coronary plaques, it primarily assesses the percentage of luminal stenosis and cannot identify HRP features that predict rupture.[10] CTCA offers distinct advantages over internal carotid artery in plaque characterization and the identification of HRP.[10] It is now recognized as the preferred initial imaging modality for patients at risk of ACS due to its high negative predictive value.[27] The identification of a necrotic core within HRPs and changes in perivascular fat attenuation are expected to emerge as important imaging biomarkers for ACS in the near future.[26] [28] Perivascular fat attenuation provides incremental prognostic value beyond clinical characteristics and have potential role in the prediction of major adverse cardiovascular events.[29] [30]

One of the primary limitations of our study is the relatively small sample size, which may reduce the statistical power and limit the generalizability of our findings. Due to the restricted cohort, subgroup analysis—particularly for HRP features—was constrained. Additionally, this study was conducted at a single tertiary care center, which may introduce selection bias and reduce the external validity. Technical variability, including image acquisition parameters and differences in reconstruction algorithms, could also have influenced the interpretation of perivascular fat attenuation and plaque morphology. Lastly, the absence of long-term clinical follow-up limited our ability to correlate imaging biomarkers with cardiovascular outcomes. As a result, the findings should be interpreted with larger, multicenter studies, which would warrant to validate these preliminary observations.

Conclusion

This study is the first from India to evaluate coronary Perivascular Fat Attenuation Ratio (PVFAR), a promising non-invasive marker of coronary inflammation and high-risk plaques. Unlike invasive CAG, which is limited in characterizing plaque vulnerability, CTCA combined with FAI analysis offers valuable insight into plaque composition and inflammation. The ability of PVAT attenuation—especially through lesion-specific ratios—to differentiate HRPs from stable ones provides incremental prognostic value and may enhance risk stratification for ACS. As imaging technology evolves, PVAT-based metrics are poised to play a pivotal role in guiding personalized treatment strategies and predicting major adverse cardiovascular event. These parameters could serve as an additional diagnostic criterion for identifying HRP in CAD-RADS 3.0.

Conflict of Interest

None declared.

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Publication History

Article published online:
11 February 2026