Semiautomated Characterization of Carotid Artery Plaque Features From Computed Tomography Angiography to Predict Atherosclerotic Cardiovascular Disease Risk Score

Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography

BACKGROUND

This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis.

METHODS

The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration.

RESULTS

This retrospective, single-center study evaluated computed tomography angiography scans of 268 patients with both symptomatic and asymptomatic carotid atherosclerosis (163 for the derivation set and 106 for the testing set) performed between March 2013 and October 2019. The area-under-receiver-operating characteristics curve by machine learning on the testing cohort (0.89) was significantly higher than the areas under the curve of traditional logit analysis based on the degree of stenosis (0.51, P<0.001), presence of intraplaque hemorrhage (0.69, P<0.001), and plaque composition (0.78, P<0.001), respectively. Comparable performance was obtained on internal validation. The identified plaque components and associated cutoff values that were significantly associated with a higher likelihood of symptomatic status after adjustment were the ratio of intraplaque hemorrhage to lipid volume (≥50%, 38.5 [10.1-205.1]; odds ratio, 95% CI) and percentage of intraplaque hemorrhage volume (≥10%, 18.5 [5.7-69.4]; odds ratio, 95% CI).

CONCLUSIONS

This study presented an interpretable machine learning model that accurately identifies symptomatic carotid plaques using computed tomography angiography derived plaque composition features, aiding clinical decision-making.

Value of 2D speckle tracking technique combined with real-time 3-dimensional echocardiography in the evaluation of the right atrial function in patients with 3-branch coronary artery disease without myocardial infarction

To evaluate the right atrial function in patients with 3-branch coronary artery disease (TBCAD) without myocardial infarction by 2D speckle tracking echocardiography (2D-STE) combined with real-time 3-dimensional echocardiography (RT-3DE). Fifty-six patients admitted to our hospital without myocardial infarction with TBCAD were selected. We divided them into 2 groups according to the coronary angiography results: 28 patients in group B (the rate of stenosis is 50% ~< 75%); 28 patients in group C (the rate of stenosis is ≥75%); in addition, 30 healthy volunteers were screened as group A. All subjects underwent RT-3DE to obtain the right atrial volume (RAVmax, RAVmin, and RAVp), and then we calculated the right atrial passive and active ejection fraction (RAPEF, RAAEF), and maximum volume index (RAVImax). In addition, to measure the strain rates (RASRs, RASRe, RASRa) of the right atrium during systole, early diastole, and late diastole, 2D-STE was applied. Correlations between the 2D-STE parameters and the results of N-terminal pro-brain natriuretic peptide (NT-proBNP) and Gensini scores were analyzed by Pearson linear analysis. Compared with group A, RAPEF and RASRe were reduced, while RAAEF and RASRa were elevated in group B (P < .05). RAPEF, RASRs, RASRe, and RASRa were decreased compared with groups A and B, while RAVmax, RAVmin, RAVp, RAVImax, and RAAEF were increased in group C (P < .05). There was a significant correlation between 2D-STE parameters and the results of NT-proBNP and Gensini scores (P < .05). The storage, conduit, and pump functions of the right atrium are reduced in patients with 3-branch coronary artery disease without myocardial infarction; 2D-STE combined with RT-3DE is valuable in the evaluation of the right atrium in patients with coronary artery disease.

Contrast-Enhanced Ultrasound Feasibility in Assessing Carotid Plaque Vulnerability-Narrative Review

The risk assessment for carotid atherosclerotic lesions involves not only determining the degree of stenosis but also plaque morphology and its composition. Recently, carotid contrast-enhanced ultrasound (CEUS) has gained importance for evaluating vulnerable plaques. This review explores CEUS's utility in detecting carotid plaque surface irregularities and ulcerations as well as intraplaque neovascularization and its alignment with histology. Initial indications suggest that CEUS might have the potential to anticipate cerebrovascular incidents. Nevertheless, there is a need for extensive, multicenter prospective studies that explore the relationships between CEUS observations and patient clinical outcomes in cases of carotid atherosclerotic disease.

Quantitative imaging biomarkers of coronary plaque morphology: insights from EVAPORATE

Aims

Residual cardiovascular risk persists despite statin therapy. In REDUCE-IT, icosapent ethyl (IPE) reduced total events, but the mechanisms of benefit are not fully understood. EVAPORATE evaluated the effects of IPE on plaque characteristics by coronary computed tomography angiography (CCTA). Given the conclusion that the IPE-treated patients demonstrate that plaque burden decreases has already been published in the primary study analysis, we aimed to demonstrate whether the use of an analytic technique defined and validated in histological terms could extend the primary study in terms of whether such changes could be reliably seen in less time on drug, at the individual (rather than only at the cohort) level, or both, as neither of these were established by the primary study result.

Methods and Results

EVAPORATE randomized the patients to IPE 4 g/day or placebo. Plaque morphology, including lipid-rich necrotic core (LRNC), fibrous cap thickness, and intraplaque hemorrhage (IPH), was assessed using the ElucidVivo® (Elucid Bioimaging Inc.) on CCTA. The changes in plaque morphology between the treatment groups were analyzed. A neural network to predict treatment assignment was used to infer patient representation that encodes significant morphological changes. Fifty-five patients completed the 18-month visit in EVAPORATE with interpretable images at each of the three time points. The decrease of LRNC between the patients on IPE vs. placebo at 9 months (reduction of 2 mm3 vs. an increase of 41 mm3, p = 0.008), widening at 18 months (6 mm3 vs. 58 mm3 increase, p = 0.015) were observed. While not statistically significant on a univariable basis, reductions in wall thickness and increases in cap thickness motivated multivariable modeling on an individual patient basis. The per-patient response assessment was possible using a multivariable model of lipid-rich phenotype at the 9-month follow-up, p < 0.01 (sustained at 18 months), generalizing well to a validation cohort.

Conclusion

Plaques in the IPE-treated patients acquired more characteristics of stability. Reliable assessment using histologically validated analysis of individual response is possible at 9 months, with sustained stabilization at 18 months, providing a quantitative basis to elucidate drug mechanism and assess individual patient response.

Atherosclerosis risk classification with computed tomography angiography: A radiologic-pathologic validation study

BACKGROUND AND AIMS

The application of machine learning to assess plaque risk phenotypes on cardiovascular CT angiography (CTA) is an area of active investigation. Studies using accepted histologic definitions of plaque risk as ground truth for machine learning models are uncommon. The aim was to evaluate the accuracy of a machine-learning software for determining plaque risk phenotype as compared to expert pathologists (histologic ground truth).

METHODS

Sections of atherosclerotic plaques paired with CTA were prospectively collected from patients undergoing carotid endarterectomy at two centers. Specimens were annotated for lipid-rich necrotic core, calcification, matrix, and intraplaque hemorrhage at 2 mm spacing and classified as minimal disease, stable plaque, or unstable plaque according to a modified American Heart Association histological definition. Phenotype is determined in two steps: plaque morphology is delineated according to histological tissue definitions, followed by a machine learning classifier. The performance in derivation and validation cohorts for plaque risk categorization and stenosis was compared to histologic ground truth at each matched cross-section.

RESULTS

A total of 496 and 408 vessel cross-sections in the derivation and validation cohorts (from 30 and 23 patients, respectively). The software demonstrated excellent agreement in the validation cohort with histological ground truth plaque risk phenotypes with weighted kappa of 0.82 [0.78-0.86] and area under the receiver operating curve for correct identification of plaque type was 0.97 [0.96, 0.98], 0.95 [0.94, 0.97], 0.99 [0.99, 1.0] for unstable plaque, stable plaque, and minimal disease, respectively. Diameter stenosis correlated poorly to histologically defined plaque type; weighted kappa 0.25 in the validation cohort.

CONCLUSIONS

A machine-learning software trained on histological ground-truth tissue inputs demonstrated high accuracy for identifying plaque stability phenotypes as compared to expert pathologists.

Virtual pathology: Reaching higher standards for noninvasive CTA tissue characterization capability by using histology as a truth standard

AIMS

Despite advances in therapy, reduction in myocardial infarction or death remains elusive. Whereas computed tomography angiography (CTA) is increasingly appreciated, the analyses are often subjective or qualitative. Methods for specific tissue characterization using histopathologic correlates have recently been reported. We extend this here to demonstrate accurate discrimination between, and quantitation of, lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), and fibrotic tissues.

METHODS

NCT02143102 collected 576 tissue samples with paired CTA. Cardiovascular pathologists annotated LRNC, IPH, and dense calcification (CALC) regions as a reference standard. Blinded to histology, CTA was analyzed using ElucidVivo (Elucid Bioimaging Inc., Boston, MA USA). Structure and tissue characteristics of atherosclerotic plaque from CTA, accounting for both the imaging acquisition process and the biology, accounting for differences in density distributions that result from the different cellular and molecular level milieu of the relevant tissue types.

RESULTS

LRNC was tested across a true range of 0-10 mm², with a difference of 0.15 mm² and a slope of 0.92. IPH was tested across a true range of 0-18 mm², with a difference from histology of 1.68 mm² and a slope of 0.95. CALC was tested across a range of 0-14 mm², with a difference of -0.06 mm² and a slope of 0.99. Matrix tissue (MATX) was tested across a range of 4-52 mm², with a difference of 0.02 mm² and a slope of 0.91.

CONCLUSION

LRNC, IPH, CALC, and MATX may be objectively quantified using histopathologic correlates automatically from CTA for use singly or in combination to optimize patient care. The availability of objectively validated quantitative markers that may be followed longitudinally may extend the clinical utility of CTA. Additionally, these measures contribute efficacy variables for developing novel drugs and clinical decision support tools for tailored therapeutics.

Radiomics assessment of carotid intraplaque hemorrhage: detecting the vulnerable patients

BACKGROUND

Intraplaque hemorrhage (IPH), one of the key features of vulnerable plaques, has been shown to be associated with increased risk of stroke. The aim is to develop and validate a CT-based radiomics nomogram incorporating clinical factors and radiomics signature for the detection of IPH in carotid arteries.

METHODS

This retrospective study analyzed the patients with carotid plaques on CTA from January 2013 to January 2021 at two different institutions. Radiomics features were extracted from CTA images. Demographics and CT characteristics were evaluated to build a clinical factor model. A radiomics signature was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. The area under curves of three models were calculated by receiver operating characteristic analysis.

RESULTS

A total of 46 patients (mean age, 60.7 years ± 10.4 [standard deviation]; 36 men) with 106 carotid plaques were in the training set, and 18 patients (mean age, 61.4 years ± 10.1; 13 men) with 38 carotid plaques were in the external test sets. Stenosis was the independent clinical factor. Eight features were used to build the radiomics signature. The area under the curve (AUC) of the radiomics nomogram was significantly higher than that of the clinical factor model in both the training (p = 0.032) and external test (p = 0.039) sets.

CONCLUSIONS

A CT-based radiomics nomogram showed satisfactory performance in distinguishing carotid plaques with and without intraplaque hemorrhage.

Biomechanical Assessment of Macro-Calcification in Human Carotid Atherosclerosis and Its Impact on Smooth Muscle Cell Phenotype

Intimal calcification and vascular stiffening are predominant features of end-stage atherosclerosis. However, their role in atherosclerotic plaque instability and how the extent and spatial distribution of calcification influence plaque biology remain unclear. We recently showed that extensive macro calcification can be a stabilizing feature of late-stage human lesions, associated with a reacquisition of more differentiated properties of plaque smooth muscle cells (SMCs) and extracellular matrix (ECM) remodeling. Here, we hypothesized that biomechanical forces related to macro-calcification within plaques influence SMC phenotype and contribute to plaque stabilization. We generated a finite element modeling (FEM) pipeline to assess plaque tissue stretch based on image analysis of preoperative computed tomography angiography (CTA) of carotid atherosclerotic plaques to visualize calcification and soft tissues (lipids and extracellular matrix) within the lesions. Biomechanical stretch was significantly reduced in tissues in close proximity to macro calcification, while increased levels were observed within distant soft tissues. Applying this data to an in vitro stretch model on primary vascular SMCs revealed upregulation of typical markers for differentiated SMCs and contractility under low stretch conditions but also impeded SMC alignment. In contrast, high stretch conditions in combination with calcifying conditions induced SMC apoptosis. Our findings suggest that the load bearing capacities of macro calcifications influence SMC differentiation and survival and contribute to atherosclerotic plaque stabilization.

Identification Markers of Carotid Vulnerable Plaques: An Update

Vulnerable plaques have been a hot topic in the field of stroke and carotid atherosclerosis. Currently, risk stratification and intervention of carotid plaques are guided by the degree of luminal stenosis. Recently, it has been recognized that the vulnerability of plaques may contribute to the risk of stroke. Some classical interventions, such as carotid endarterectomy, significantly reduce the risk of stroke in symptomatic patients with severe carotid stenosis, while for asymptomatic patients, clinically silent plaques with rupture tendency may expose them to the risk of cerebrovascular events. Early identification of vulnerable plaques contributes to lowering the risk of cerebrovascular events. Previously, the identification of vulnerable plaques was commonly based on imaging technologies at the macroscopic level. Recently, some microscopic molecules pertaining to vulnerable plaques have emerged, and could be potential biomarkers or therapeutic targets. This review aimed to update the previous summarization of vulnerable plaques and identify vulnerable plaques at the microscopic and macroscopic levels.

Patient-specific biomechanical analysis of atherosclerotic plaques enabled by histologically validated tissue characterization from computed tomography angiography: A case study

BACKGROUND

Rupture of unstable atherosclerotic plaques with a large lipid-rich necrotic core and a thin fibrous cap cause myocardial infarction and stroke. Yet it has not been possible to assess this for individual patients. Clinical guidelines still rely on use of luminal narrowing, a poor indicator but one that persists for lack of effective means to do better. We present a case study demonstrating the assessment of biomechanical indices pertaining to plaque rupture risk non-invasively for individual patients enabled by histologically validated tissue characterization.

METHODS

Routinely acquired clinical images of plaques were analyzed to characterize vascular wall tissues using software validated by histology (ElucidVivo, Elucid Bioimaging Inc.). Based on the tissue distribution, wall stress and strain were then calculated at spatial locations with varied fibrous cap thicknesses at diastolic, mean and systolic blood pressures.

RESULTS

The von Mises stress of 152 [131, 172] kPa and the equivalent strain of 0.10 [0.08, 0.12] were calculated where the fibrous cap thickness was smallest (560 μm) (95% CI in brackets). The stress at this location was at a level predictive of plaque failure. Stress and strain at locations with larger cap thicknesses were calculated to be lower, demonstrating a clinically relevant range of risk levels.

CONCLUSION

Patient specific tissue characterization can identify distributions of stress and strain in a clinically relevant range. This capability may be used to identify high-risk lesions and personalize treatment decisions for individual patients with cardiovascular disease and improve prevention of myocardial infarction and stroke.

CT angiographic biomarkers help identify vulnerable carotid artery plaque

OBJECTIVE

Current risk assessment for patients with carotid atherosclerosis relies primarily on measuring the degree of stenosis. More reliable risk-stratification could improve patient selection for targeted treatment. We developed and validated a model to predict major adverse neurological events (MANE; stroke, transient ischemic attack, and amaurosis fugax) incorporating a combination of plaque morphology, patient demographics, and patient clinical information.

METHODS

We enrolled 221 patients with asymptomatic carotid stenosis of any severity who had CT angiography at baseline and at least 6 months later. Images were analyzed for carotid plaque morphology (plaque geometry and tissue composition). Data were partitioned (training and validation cohorts). 190 patients had complete records and were advanced to analysis. The training cohort was used to develop the best model for predicting MANE, incorporating patient and plaque features. First, single-variable correlation and unsupervised clustering were performed. Next, several multi-variable models were implemented for the response variable of MANE. The best model was selected by optimizing area under the receiver operating characteristic curve (AUC, ROC) and Kappa. The model was validated on the sequestered data to demonstrate generalizability.

RESULTS

Sixty-two patients suffered a MANE on follow-up. Unsupervised clustering of patient and plaque features identified single-variable predictors of MANE. Multi-variable predictive modeling showed that a combination of plaque features at baseline (matrix, intra-plaque hemorrhage (IPH), wall thickness, plaque burden) with clinical features (age, BMI, lipid levels) best predicted MANE (AUC 0.79), while percent diameter stenosis performed worst (AUC 0.55). The strongest single variable in discriminating between patients with and without events was IPH, and the most predictive model was produced when IPH was considered together with wall remodeling. The selected model also performed well on the validation dataset (AUC of 0.64) and maintained superiority over percent diameter stenosis (AUC of 0.49).

CONCLUSIONS

A composite of plaque geometry, plaque tissue composition, patient demographics, and clinical information predicts MANE better than the traditionally utilized degree of stenosis alone in carotid atherosclerosis. Implementing this predictive model in the clinical setting can help identify patients at high-risk for major adverse neurological events.

A Machine Learning Model for the Identification of High risk Carotid Atherosclerotic Plaques

Carotid artery disease is an inflammatory condition involving the deposition and accumulation of lipid species and leucocytes from blood into the arterial wall, which causes the narrowing of the carotid arteries on either side of the neck. Different imaging modalities can by implemented to determine the presence and the location of carotid artery stenosis, such as carotid ultrasound, computed tomography angiography (CTA), magnetic resonance angiography (MRA), or cerebral angiography. However, except of the presence and the degree of stenosis of the carotid arteries, the vulnerability of the carotid atherosclerotic plaques constitutes a significant factor for the progression of the disease and the presence of disease symptoms. In this study, our aim is to develop and present a machine learning model for the identification of high risk plaques using non imaging based features and non-invasive imaging based features. Firstly, we implemented statistical analysis to identify the most statistical significant features according to the defined output, and subsequently, we implemented different feature selection techniques and classification schemes for the development of our machine learning model. The overall methodology has been trained and tested using 208 cases of 107 cases of low risk plaques and 101 cases of high risk plaques. The highest accuracy of 0.76 was achieved using the relief feature selection technique and the support vector machine classification scheme. The innovative aspect of the proposed machine learning model is both the different categories of the utilized input features and the definition of the problem to be solved.

Carotid Plaque Phenotyping by Correlating Plaque Morphology from Computed Tomography Angiography with Transcriptional Profiling

OBJECTIVE

Ischaemic strokes can be caused by unstable carotid atherosclerosis, but methods for identification of high risk lesions are lacking. Carotid plaque morphology imaging using software for visualisation of plaque components in computed tomography angiography (CTA) may improve assessment of plaque phenotype and stroke risk, but it is unknown if such analyses also reflect the biological processes related to lesion stability. Here, we investigated how carotid plaque morphology by image analysis of CTA is associated with biological processes assessed by transcriptomic analyses of corresponding carotid endarterectomies (CEAs).

METHODS

Carotid plaque morphology was assessed in patients undergoing CEA for symptomatic or asymptomatic carotid stenosis consecutively enrolled between 2006 and 2015. Computer based analyses of pre-operative CTA was performed to define calcification, lipid rich necrotic core (LRNC), intraplaque haemorrhage (IPH), matrix (MATX), and plaque burden. Plaque morphology was correlated with molecular profiles obtained from microarrays of corresponding CEAs and models were built to assess the ability of plaque morphology to predict symptomatology.

RESULTS

Carotid plaques (n = 93) from symptomatic patients (n = 61) had significantly higher plaque burden and LRNC compared with plaques from asymptomatic patients (n = 32). Lesions selected from the transcriptomic cohort (n = 40) with high LRNC, IPH, MATX, or plaque burden were characterised by molecular signatures coupled with inflammation and extracellular matrix degradation, typically linked with instability. In contrast, highly calcified plaques had a molecular signature signifying stability with enrichment of profibrotic pathways and repressed inflammation. In a cross validated prediction model for symptoms, plaque morphology by CTA alone was superior to the degree of stenosis.

CONCLUSION

The study demonstrates that CTA image analysis for evaluation of carotid plaque morphology, also reflects prevalent biological processes relevant for assessment of plaque phenotype. The results support the use of CTA image analysis of plaque morphology for risk stratification and management of patients with carotid stenosis.

Correlation between computed tomography angiography and histology of carotid artery atherosclerosis: Can semi-automated imaging software predict a plaque's composition?

BACKGROUND

Using computed tomography angiography to differentiate between components of carotid atherosclerotic lesions remains largely elusive. This study sought to validate a semi-automated software for computed tomography angiography plaque analysis using histologic comparisons.

MATERIALS AND METHODS

A retrospective review was performed of consecutive patients that underwent a carotid endarterectomy, with pre-procedural computed tomography angiography imaging of the cervical arterial vasculature available for review. Images were evaluated using a commercially-available software package, which produced segmented analyses of intraplaque components (e.g. intraplaque hemorrhage, lipid-rich necrotic core, and calcifications). On imaging, each component was assessed in terms of its (1) presence or absence, and (2) both volume and proportion of the total plaque volume (if present). On histological evaluation of carotid endarterectomy specimens, each component was evaluated as an estimated proportion of total plaque volume.

RESULTS

Of 80 included patients, 30 (37.5%) were female. The average age was 69.7 years (SD = 9.1). Based on imaging, intraplaque hemorrhage was the smallest contributor to plaque composition (1.2% of volumes on average). Statistically significant linear associations were noted between the proportion of intraplaque hemorrhage, lipid-rich necrotic core, and calcifications on histology and the volume of each component on imaging (p values ranged from 0.0008 to 0.01). Area under curve were poor for intraplaque hemorrhage and lipid-rich necrotic core (0.59 and 0.61, respectively) and acceptable for calcifications (0.73).

CONCLUSION

Semi-automated analyses of computed tomography angiography have limited diagnostic accuracy in the detection of intraplaque hemorrhage and lipid-rich necrotic core in carotid artery plaques. However, volumetric imaging measurements of different components corresponded with histologic analysis.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Proteoglycan 4 Modulates Osteogenic Smooth Muscle Cell Differentiation during Vascular Remodeling and Intimal Calcification

Calcification is a prominent feature of late-stage atherosclerosis, but the mechanisms driving this process are unclear. Using a biobank of carotid endarterectomies, we recently showed that Proteoglycan 4 (PRG4) is a key molecular signature of calcified plaques, expressed in smooth muscle cell (SMC) rich regions. Here, we aimed to unravel the PRG4 role in vascular remodeling and intimal calcification. PRG4 expression in human carotid endarterectomies correlated with calcification assessed by preoperative computed tomographies. PRG4 localized to SMCs in early intimal thickening, while in advanced lesions it was found in the extracellular matrix, surrounding macro-calcifications. In experimental models, Prg4 was upregulated in SMCs from partially ligated ApoE-/- mice and rat carotid intimal hyperplasia, correlating with osteogenic markers and TGFb1. Furthermore, PRG4 was enriched in cells positive for chondrogenic marker SOX9 and around plaque calcifications in ApoE-/- mice on warfarin. In vitro, PRG4 was induced in SMCs by IFNg, TGFb1 and calcifying medium, while SMC markers were repressed under calcifying conditions. Silencing experiments showed that PRG4 expression was driven by transcription factors SMAD3 and SOX9. Functionally, the addition of recombinant human PRG4 increased ectopic SMC calcification, while arresting cell migration and proliferation. Mechanistically, it suppressed endogenous PRG4, SMAD3 and SOX9, and restored SMC markers' expression. PRG4 modulates SMC function and osteogenic phenotype during intimal remodeling and macro-calcification in response to TGFb1 signaling, SMAD3 and SOX9 activation. The effects of PRG4 on SMC phenotype and calcification suggest its role in atherosclerotic plaque stability, warranting further investigations.

Virtual Transcriptomics: Noninvasive Phenotyping of Atherosclerosis by Decoding Plaque Biology From Computed Tomography Angiography Imaging

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Semiautomated carotid artery plaque composition: are intraplaque CT imaging features associated with cardiovascular risk factors?

BACKGROUND

Little remains known about the connection between cardiovascular (CV) risk factors and carotid plaque morphologies. This study set out to assess for any such associations.

MATERIALS AND METHODS

A retrospective review was completed of consecutive patients that had CTA neck imaging prior to CEA. Body mass index (BMI), tobacco and/or alcohol use, and history of diabetes and/or hypertension were collected from patients' medical records. Lab values were dichotomized based on values: total cholesterol < 200 or ≥ 200; low-density lipoprotein (LDL) < 130 or ≥ 130, high-density lipoprotein < 35 or ≥ 35, and triglycerides < 200 or ≥ 200. A semiautomated analysis of CTA images computed maximum stenosis, intraplaque volumes of intraplaque hemorrhage, lipid-rich necrotic core (LRNC), and matrix, and intraplaque volume and proportional plaque makeup of calcifications of each carotid plaque.

RESULTS

Of 87 included patients, 54 (62.1%) were male. Mean age was 70.1 years old. Both diabetes and hypertension were associated with greater intraplaque calcification volume (p = 0.0009 and p = 0.01, respectively), and greater proportion of calcification within a plaque (p = 0.004 and p = 0.01, respectively). Higher BMI was associated with greater intraplaque volume of LRNC (p=0.02) and matrix (0.0007). Elevated total cholesterol was associated with both larger intraplaque calcification volume (p = 0.04) and greater proportion of calcification within a plaque (p = 0.01); elevated LDL was associated with greater intraplaque calcification volume (p = 0.005).

CONCLUSION

Multiple CV risk factors are associated with morphological differences in carotid artery plaques. Dysregulation of both total cholesterol and LDL and higher BMI are associated with higher volumes of intraplaque LRNC, a marker of plaque vulnerability.

Coronary plaque assessment of Vasodilative capacity by CT angiography effectively estimates fractional flow reserve

BACKGROUND

To evaluate the feasibility of non-invasive fractional flow reserve (FFR) estimation using histologically-validated assessment of plaque morphology on coronary CTA (CCTA) as inputs to a predictive model further validated against invasive FFR.

METHODS

Patients (n = 113, 59 ± 8.9 years, 77% male) with suspected coronary artery disease (CAD) who had undergone CCTA and invasive FFR between August 2013 and May 2018 were included. Commercially available software was used to extract quantitative plaque morphology inclusive of both vessel structure and composition. The extracted plaque morphology was then fed as inputs to an optimized artificial neural network to predict lesion-specific ischemia/hemodynamically significant CAD with performance validated by invasive FFR.

RESULTS

A total of 122 lesions were considered, 59 (48%) had low FFR values. Plaque morphology-based FFR assessment achieved an area under the curve, sensitivity and specificity of 0.94, 0.90 and 0.81, respectively, versus 0.71, 0.71, and 0.50, respectively, for an optimized threshold applied to degree of stenosis. The optimized ridge regression model for continuous value estimation of FFR achieved a cross-correlation coefficient of 0.56 and regression slope of 0.59 using cross validation, versus 0.18 and 0.10 for an optimized threshold applied to degree of stenosis.

CONCLUSIONS

Our results show that non-invasive plaque morphology-based FFR assessment may be used to predict lesion-specific ischemia resulting in hemodynamically significant CAD. This substantially outperforms degree of stenosis interpretation and has a comparable level of sensitivity and specificity relative to publicly reported results from computational fluid dynamics-based approaches.

CT imaging features of carotid artery plaque vulnerability

Despite steady advances in medical care, cardiovascular disease remains one of the main causes of death and long-term morbidity worldwide. Up to 30% of strokes are associated with the presence of carotid atherosclerotic plaques. While the degree of stenosis has long been recognized as the main guiding factor in risk stratification and therapeutical decisions, recent evidence suggests that features of unstable, or ‘vulnerable’, plaques offer better prognostication capabilities. This paradigmatic shift has motivated researchers to explore the potentialities of non-invasive diagnostic tools to image not only the lumen, but also the vascular wall and the structural characteristics of the plaque. The present review will offer a panoramic on the imaging modalities currently available to characterize carotid atherosclerotic plaques and, in particular, it will focus on the increasingly important role covered by multidetector computed tomographic angiography.

Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease

Carotid artery plaque is a measure of atherosclerosis and is associated with future risk of atherosclerotic cardiovascular disease (ASCVD), which encompasses coronary, cerebrovascular, and peripheral arterial diseases. With advanced imaging techniques, computerized tomography (CT) and magnetic resonance imaging (MRI) have shown their potential superiority to routine ultrasound to detect features of carotid plaque vulnerability, such as intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC), and calcification. The correlation between imaging features and histological changes of carotid plaques has been investigated. Imaging of carotid features has been used to predict the risk of cardiovascular events. Other techniques such as nuclear imaging and intra-vascular ultrasound (IVUS) have also been proposed to better understand the vulnerable carotid plaque features. In this article, we review the studies of imaging specific carotid plaque components and their correlation with risk scores.

Plaque imaging volume analysis: technique and application

The prevention and management of atherosclerosis poses a tough challenge to public health organizations worldwide. Together with myocardial infarction, stroke represents its main manifestation, with up to 25% of all ischemic strokes being caused by thromboembolism arising from the carotid arteries. Therefore, a vast number of publications have focused on the characterization of the culprit lesion, the atherosclerotic plaque. A paradigm shift appears to be taking place at the current state of research, as the attention is gradually moving from the classically defined degree of stenosis to the identification of features of plaque vulnerability, which appear to be more reliable predictors of recurrent cerebrovascular events. The present review will offer a perspective on the present state of research in the field of carotid atherosclerotic disease, focusing on the imaging modalities currently used in the study of the carotid plaque and the impact that such diagnostic means are having in the clinical setting.

Carotid Intraplaque-Hemorrhage Volume and Its Association with Cerebrovascular Events

BACKGROUND AND PURPOSE

Our aim was to assess the relationship between volume and percentage of intraplaque hemorrhage measured using CT and the occurrence of cerebrovascular events at the time of CT.

MATERIALS AND METHODS

One-hundred-twenty-three consecutive subjects (246 carotid arteries) with a mean age of 69 years who underwent CTA were included in this retrospective study. Plaque volume of components and subcomponents (including intraplaque hemorrhage volume) was quantified with dedicated software.

RESULTS

Forty-six arteries were excluded because no plaque was identified. In the remaining 200 carotid arteries, a statistically significant difference was found between presentation with cerebrovascular events and lipid volume (P = .002), intraplaque hemorrhage volume (P = .002), percentage of lipid (P = .002), percentage of calcium (P = .001), percentage of intraplaque hemorrhage (P = .001), percentage of lipid-intraplaque hemorrhage (P = .001), and intraplaque hemorrhage/lipid ratio (P = .001). The highest receiver operating characteristic area under the curve was obtained with the intraplaque hemorrhage volume with a value of 0.793 (P = .001), percentage of intraplaque hemorrhage with an area under the curve of 0.812 (P = .001), and the intraplaque hemorrhage/lipid ratio with an area under the curve value of 0.811 (P = .001).

CONCLUSIONS

Results of our study suggest that Hounsfield unit values <25 have a statistically significant association with the presence of cerebrovascular events and that the ratio intraplaque hemorrhage/lipid volume represents a strong parameter for the association of cerebrovascular events.

Carotid Plaque CTA Analysis in Symptomatic Subjects with Bilateral Intraparenchymal Hemorrhage: A Preliminary Analysis

BACKGROUND AND PURPOSE

The presence of IPH is considered the most dangerous feature because it is significantly associated with clinical ipsilateral cerebrovascular events. Our aim was to explore the characterization of plaque with CT in symptomatic subjects with bilateral intraplaque hemorrhage.

MATERIALS AND METHODS

Three-hundred-forty-three consecutive patients with recent anterior circulation ischemic events (<2 weeks) and CT of the carotid arteries (performed within 14 days of the cerebrovascular event) evaluated between June 2012 and September 2017 were analyzed for plaque volume composition to identify all subjects with bilateral intraplaque hemorrhage. Plaque volume was semiautomatically measured, and tissue components were classified according to the attenuation values such as the following: calcified (for values of ≥130 HU), mixed (for values of ≥60 and <130 HU), lipid (for values of ≥25 and <60 HU), and intraplaque hemorrhage (for values of <25 HU). Twenty-one subjects (15 men; mean age, 70 ± 11 years; range, 44-87 years) had bilateral intraplaque hemorrhage and were included in the analysis.

RESULTS

Volume measurement revealed significantly larger plaques on the symptomatic side compared with the asymptomatic one (mean, 28 ± 9 versus 22 ± 8 mm, P = .007). Intraplaque hemorrhage volume and percentage were also significantly higher in the plaque ipsilateral to the cerebrovascular event (P < .001 and < .001, respectively). The volume of other plaque components did not show a statically significant association except for lipid and lipid + intraplaque hemorrhage percentages (23% versus 18% and 11% versus 15%), which were significantly different between the symptomatic and the asymptomatic sides (.016 and .011, respectively). The intraplaque hemorrhage/lipid ratio was higher on the symptomatic side (0.596 versus 0.171, P = .001).

CONCLUSIONS

In patients with bilateral intraplaque hemorrhage and recent ischemic symptoms, the plaque ipsilateral to the symptomatic side has significantly larger volume and a higher percentage of intraplaque hemorrhage compared with the contralateral, asymptomatic side.