Identification Of High-risk Plaques Research Articles

Mechanical Assessment in Atherosclerosis Based on Photoacoustic Viscoelasticity Imaging

Early identification of vulnerable plaques is a major challenge in diagnosis and assessment of atherosclerosis. In atherosclerotic plaque development, the proportion change in components caused plaque mechanical property change and induced plaque rupture. In this paper, a photoacoustic viscoelasticity imaging (PAVEI) technique was proposed to measure the viscosity–elasticity ratio of atherosclerotic plaque and evaluated for the potential in characterizing vulnerable plaques. Apolipoprotein E-knockout mice fed with a high-fat/high-cholesterol diet were chosen as the atherosclerotic model. Plaque component phantoms were examined to demonstrate the high efficiency of PAVEI in detecting the proportion change in components compared to single elasticity or viscosity detection. Finally, atherosclerotic plaques from mice aortas at different stages were imaged by PAVEI, which provided an insight into the compositional and functional characterization of vulnerability plaques and suggested its potential applications in the identification of high-risk plaques.

Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease

BackgroundThe presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence.MethodsConsecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features.ResultsHRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively.ConclusionsIn patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence.Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

Incremental value of physiological indices to predict high-risk plaque detected by NIRS-IVUS

Abstract Background The vulnerable plaque are called high-risk plaques (HRPs), and coronary computed tomography angiography (CCTA) studies have revealed the following characteristics: (1) lesion plaque density on CT≦30HU (2) positive remodeling, (3) The presence of spotty calcification, (4) ΔFractional Flow Reserve derived from CCTA (FFRCT) or Δ Quantitative Flow Reserve (QFR)≧0.06 has been cited as a feature of these lesions. Therefore, identification of HRPs in non-culprit lesion is important to reduce future cardiovascular events. Purpose The purpose of the study was to determine whether HRP is predictable by incremental values of physiological indices in non-culprit lesions of ACS subjects. Methods A total of 137 non-culprit coronary lesions in 31 ACS subjects were retrospectively evaluated by CCTA, FFRCT, QFR and NIRS-IVUS imaging. Each coronary artery was divided into 30mm long segments beginning at the ostium to evaluate lesion vulnerability. By NIRS imaging, plaque with 4-mm maximum lipid-core burden index (maxLCBI4mm) ≥400 was defined as HRP. The relationship between HRP detected by NIRS and CCTA-derived plaque features, delta (Δ) FFRCT, and ΔQFR was evaluated. Results CT density, positive remodeling, diameter stenosis, ΔFFRCT, and ΔQFR were significantly associated with HRP, respectively (P<0.05). On multivariable analysis, CT density (OR; 0.95, 95%CI 0.91-0.99, P=0.0001), diameter stenosis (OR; 1.05, 95%CI 1.00-1.10, P=0.03) and ΔQFR (OR; 2119483 , 95%CI 1.75-2561856632475.6, P=0.03) independently predicted HRP, but not ΔFFRCT. ROC analysis for predicting HRP showed greater accuracy with incremental ΔQFR values in the model of CT density and diameter stenosis (AUC 0.93, P<0.0001). Conclusion HRP detected by NIRS may be predicted with greater accuracy by integrating QFR values with the assessment of CCTA-derived plaque features.figure1figure2

Identification of vulnerable carotid plaque with CT-based radiomics nomogram

To develop and validate a radiomics nomogram for identifying high-risk carotid plaques on computed tomography (CT) angiography (CTA). A total of 280 patients with symptomatic (n=131) and asymptomatic (n=139) carotid plaques were divided into a training set (n=135), validation set (n=58), and external test set (n=87). Radiomic features were extracted from CTA images. A radiomics model was constructed based on selected features and a radiomics score (rad-score) was calculated. A clinical factor model was constructed by demographics and CT findings. A radiomics nomogram combining independent clinical factors and the rad-score was constructed. The diagnostic performance of three models was evaluated and validated by region of characteristic curves. Calcification and maximum plaque thickness were the independent clinical factors. Twenty-four features were used to build the radiomics signature. In the validation set, the nomogram (area under the curve [AUC], 0.977; 95% CI, 0.899-0.999) performed better (p=0.017 and p=0.031) than the clinical factor model (AUC, 0.862; 95% CI, 0.746-0.938) and radiomics signature (AUC, 0.944; 95% CI, 0.850-0.987). In external test set, the nomogram (AUC, 0.952; 95% CI, 0.884-0.987) and radiomics signature (AUC, 0.932; 95% CI, 0.857-0.975) showed better discrimination capability (p=0.002 and p=0.037) than clinical factor model (AUC, 0.818; 95% CI, 0.721-0.892). The CT-based nomogram showed satisfactory performance in identification of high-risk plaques in carotid arteries, and it may serve as a potential non-invasive tool to identify carotid plaque vulnerability and risk stratification.

Pericoronary adipose tissue attenuation assessed by dual-layer spectral detector computed tomography is a sensitive imaging marker of high-risk plaques.

The pericoronary fat attenuation index (FAI) derived from conventional polychromatic computed tomography (CT) can capture the presence of coronary inflammation. However, conventional polychromatic CT has limitations in material component differentiation, and spectral CT could have a better ability to discriminate tissue characteristics. Hence, this study sought to assess pericoronary adipose tissue (PCAT) attenuation using spectral CT and explore its association with atherosclerotic plaque characteristics. We enrolled 104 patients with coronary atherosclerosis who met the inclusion criteria and underwent coronary CT angiography with dual-layer spectral detector computed tomography (SDCT). Plaque anatomical characteristics were measured, and the PCAT attenuation was assessed by polychromatic images (CTpoly), virtual mono-energetic images at 40 keV (CT40 keV), the slope of spectral attenuation curve (λHU), and the effective atomic number (Zeff). The association of PCAT attenuation indicators with the presence of high-risk plaques was analyzed, along with the indicators' ability to identify high-risk plaques. PCAT attenuation indicators around high-risk plaques were higher than those around non-high-risk plaques, especially CT40 keV [-153.76±24.97 (non-high-risk plaque) vs. -119.87±22.74 (high-risk plaque), P<0.001]. CT40 keV was a predictive factor of high-risk plaques, and high CT40 keV (≥-120.60 HU) could assist in the identification of high-risk plaques, with an area under the curve of 0.883 (95% CI: 0.83-0.94, P<0.05). PCAT surrounding high-risk plaques showed higher attenuation; a finding that has been associated with coronary artery inflammation. The metrics derived from SDCT, especially CT40 keV, showed higher discriminatory power for detecting changes in PCAT attenuation than polychromatic CT. PCAT attenuation assessed by CT40 keV may provide a novel imaging marker of plaque vulnerability.

Cardiovascular risk stratification by coronary computed tomography angiography imaging: current state-of-the-art.

Current cardiovascular risk stratification by use of clinical risk score systems or plasma biomarkers is good but less than satisfactory in identifying patients at residual risk for coronary events. Recent clinical evidence puts now further emphasis on the role of coronary anatomy assessment by coronary computed tomography angiography (CCTA) for the management of patients with stable ischaemic heart disease. Available computed tomography (CT) technology allows the quantification of plaque burden, identification of high-risk plaques, or the functional assessment of coronary lesions for ischaemia detection and revascularization for refractory angina symptoms. The current CT armamentum is also further enhanced by perivascular Fat Attenuation Index (FAI), a non-invasive metric of coronary inflammation, which allows for the first time the direct quantification of the residual vascular inflammatory burden. Machine learning and radiomic features' extraction and spectral CT for tissue characterization are also expected to maximize the diagnostic and prognostic yield of CCTA. The combination of anatomical, functional, and biological information on coronary circulation by CCTA offers a unique toolkit for the risk stratification of patients, and patient selection for targeted aggressive prevention strategies. We hereby provide a review of the current state-of-the art in the field and discuss how integrating the full capacities of CCTA into clinical care pathways opens new opportunities for the tailored management of coronary artery disease.

Identification of high-risk carotid plaque with MRI-based radiomics and machine learning.

We sought to build a high-risk plaque MRI-based model (HRPMM) using radiomics features and machine learning for differentiating symptomatic from asymptomatic carotid plaques. One hundred sixty-two patients with carotid stenosis were randomly divided into training and test cohorts. Multi-contrast MRI including time of flight (TOF), T1- and T2-weighted imaging, and contrast-enhanced imaging was done. Radiological characteristics of the carotid plaques were recorded and calculated to build a traditional model. After extracting the radiomics features on these images, we constructed HRPMM with least absolute shrinkage and selection operator algorithm in the training cohort and evaluated its performance in the test cohort. A combined model was also built using both the traditional and radiomics features. The performance of all the models in the identification of high-risk carotid plaque was compared. Intraplaque hemorrhage and lipid-rich necrotic core were independently associated with clinical symptoms and were used to build the traditional model, which achieved an area under the curve (AUC) of 0.825 versus 0.804 in the training and test cohorts. The HRPMM and the combined model achieved an AUC of 0.988 versus 0.984 and of 0.989 versus 0.986 respectively in the two cohorts. Both the radiomics model and combined model outperformed the traditional model, whereas the combined model showed no significant difference with the HRPMM. Our MRI-based radiomics model can accurately distinguish symptomatic from asymptomatic carotid plaques. It is superior to the traditional model in the identification of high-risk plaques. • Carotid plaque multi-contrast MRI stores other valuable information to be further exploited by radiomics analysis. • Radiomics analysis can accurately distinguish symptomatic from asymptomatic carotid plaques. • The radiomics model is superior to the traditional model in the identification of high-risk plaques.

Automated Artificial Intelligence Based Interpretation Of Coronary CTA: Plaque Volume, Plaque Characterization And High Risk Plaque Compared With Consensus Of Level III Expert Readers

Introduction: Quantitative atherosclerosis evaluation has high clinical import, but the analysis is time-intensive and requires high expertise. Artificial intelligence (AI), through machine learning algorithms is an emerging approach to quantitative image processing and automated interpretation. We present results of the first FDA-cleared, commercially available, end-to-end AI-based solution for the automated evaluation of quantitative atherosclerotic plaque volume, characterization and identification of high-risk plaque on coronary CT angiography (CCTA).

TCT-831 Identification of High-Risk Plaques by Coronary Angiography: Confirmation of Computational Fluid Dynamics by Optical Coherence Tomography

TCT-831 Identification of High-Risk Plaques by Coronary Angiography: Confirmation of Computational Fluid Dynamics by Optical Coherence Tomography

Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics

ObjectivesThe authors investigated the utility of noninvasive hemodynamic assessment in the identification of high-risk plaques that caused subsequent acute coronary syndrome (ACS). BackgroundACS is a critical event that impacts the prognosis of patients with coronary artery disease. However, the role of hemodynamic factors in the development of ACS is not well-known. MethodsSeventy-two patients with clearly documented ACS and available coronary computed tomographic angiography (CTA) acquired between 1 month and 2 years before the development of ACS were included. In 66 culprit and 150 nonculprit lesions as a case-control design, the presence of adverse plaque characteristics (APC) was assessed and hemodynamic parameters (fractional flow reserve derived by coronary computed tomographic angiography [FFRCT], change in FFRCT across the lesion [△FFRCT], wall shear stress [WSS], and axial plaque stress) were analyzed using computational fluid dynamics. The best cut-off values for FFRCT, △FFRCT, WSS, and axial plaque stress were used to define the presence of adverse hemodynamic characteristics (AHC). The incremental discriminant and reclassification abilities for ACS prediction were compared among 3 models (model 1: percent diameter stenosis [%DS] and lesion length, model 2: model 1 + APC, and model 3: model 2 + AHC). ResultsThe culprit lesions showed higher %DS (55.5 ± 15.4% vs. 43.1 ± 15.0%; p < 0.001) and higher prevalence of APC (80.3% vs. 42.0%; p < 0.001) than nonculprit lesions. Regarding hemodynamic parameters, culprit lesions showed lower FFRCT and higher △FFRCT, WSS, and axial plaque stress than nonculprit lesions (all p values <0.01). Among the 3 models, model 3, which included hemodynamic parameters, showed the highest c-index, and better discrimination (concordance statistic [c-index] 0.789 vs. 0.747; p = 0.014) and reclassification abilities (category-free net reclassification index 0.287; p = 0.047; relative integrated discrimination improvement 0.368; p < 0.001) than model 2. Lesions with both APC and AHC showed significantly higher risk of the culprit for subsequent ACS than those with no APC/AHC (hazard ratio: 11.75; 95% confidence interval: 2.85 to 48.51; p = 0.001) and with either APC or AHC (hazard ratio: 3.22; 95% confidence interval: 1.86 to 5.55; p < 0.001). ConclusionsNoninvasive hemodynamic assessment enhanced the identification of high-risk plaques that subsequently caused ACS. The integration of noninvasive hemodynamic assessments may improve the identification of culprit lesions for future ACS. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamic [EMERALD]; NCT02374775)

Role of Cardiac PET in Clinical Practice

Early identification of atherosclerosis and at-risk lesions plays a critical role in reducing the burden of cardiovascular disease. While invasive coronary angiography serves as the gold standard for diagnosing coronary artery disease, non-invasive imaging techniques provide visualization of both anatomical and functional atherosclerotic processes prior to clinical presentation. The development of cardiac positron emission tomography (PET) has greatly enhanced our capability to diagnose and treat patients with early stages of atherosclerosis. Cardiac PET is a powerful, versatile non-invasive diagnostic tool with utility in the identification of high-risk plaques, myocardial perfusion defects, and viable myocardial tissue. Cardiac PET allows for comparisons of myocardial function both at time of rest and stress, providing accurate assessments of both myocardial perfusion and viability. Furthermore, novel PET techniques with unique radiotracers yield clinically relevant data on high-risk plaques in active progressive atherosclerosis. While PET exercise stress tests were previously difficult to perform given short radiotracer half-life, the development of the novel radiotracer Flurpiridaz F-18 provides a promising future for PET exercise stress imaging. In addition, hybrid imaging with computed tomography angiography (CTA) and cardiac magnetic resonance (CMR) provides integration of cardiac function and structure. In this review article, we discuss the principles of cardiac PET, the clinical applications of PET in diagnosing and prognosticating patients at risk for future cardiovascular events, compare PET with other non-invasive cardiac imaging modalities, and discuss future applications of PET in CVD evaluation and management.

Inter-rater reliability of carotid atherosclerotic plaque quantification by 3-dimensional sonography.

Embolization from atherosclerotic carotid plaques is the most common cause of ischemic stroke; therefore, identification of high-risk plaques by sonography is important. The aim of this study was to investigate the agreement between 2 investigators in the evaluation of sonographic parameters relating to plaque stability. The following plaque parameters were assessed: echogenicity, homogeneity, surface, maximum content, and total volume. Serial 2-dimensional (2D) image sequences were obtained. Linear motion of the probe was automatically synchronized with the electrocardiogram. The edges of the plaque in each image were manually identified by the investigators. The total plaque volume was calculated after computer transformation of 2D images into a 3-dimensinoal (3D) format. Inter-rater reliability for echogenicity, homogeneity, and the surface was assessed by the weighted κ coefficient. Parametric values were tested by a paired t test. We enrolled 30 patients (22 male; mean age ± SD, 72 ± 13 years) in the study and evaluated 28 atherosclerotic plaques. Inter-rater agreement values were as follows: homogeneity, 96% (κ = 0.84; P < .001); surface, 90% (κ = 0.77; P < .001); and echogenicity, 86% (κ = 0.60; P < .001). The significance values for plaque content and volume measurement agreement were P = .311 and .312, respectively, and the correlation coefficient was 0.808. In our study, the agreement between 2 examiners in the evaluation of 2D and 3D sonographic parameters related to plaque stability was good to excellent. The sonographic measurement of plaque volume growth was the most accurate parameter; therefore, 3D sonography may be used for risk assessment of plaques in the future.

Inflammation goes with the flow: Implications for non-invasive identification of high-risk plaque

Inflammation goes with the flow: Implications for non-invasive identification of high-risk plaque

High resolution in vivo intra-arterial imaging with optical coherence tomography

BACKGROUNDOptical coherence tomography (OCT) is a new method of catheter based micron scale imaging. OCT is analogous to ultrasound, measuring the intensity of backreflected infrared light rather than sound waves.OBJECTIVETo...