Symptomatic Carotid Plaques Research Articles

Diesel exhaust particles activate macrophages and IRF5 expression in atherosclerosis

Abstract Introduction Over 20% of global deaths related to cardiovascular disease have been attributed in part to air pollution, particularly fine particulate matter (PM2.5) (1). In highly trafficked urban areas, smaller ultrafine diesel exhaust particles (DEP) represent a significant proportion of PM2.5. DEP is associated with the progression of atherosclerosis and increased plaque susceptibility to rupture (2). We have previously shown that interferon regulatory factor 5 (IRF5) has a key role in necrotic core formation, a distinguishing feature of vulnerable atherosclerotic plaques, by rendering macrophages defective at efferocytosis (3). Pronounced elevation of IRF5 is also seen in symptomatic carotid plaques, particularly in the rupture-prone shoulder regions (4). Purpose We examined the effect of DEP on macrophage phenotype and IRF5 expression in atherosclerosis. Methods Bone marrow-derived macrophages (BMDMs) from C57BL/6 mice, differentiated with GM-CSF (to mimic inflammatory macrophages) or M-CSF (mimicking homeostatic resident macrophages) were incubated with PBS or DEP (10 µg/mL) in vitro for 18 hours. Expression of inflammatory cytokines (IL-6, IL-12α), macrophage polarisation markers CD11c (itgax, inflammatory) and CD206 (mrc1, resident anti-inflammatory) and IRF5 was quantified by real-time PCR. In addition, high fat-fed ApoE-/- mice were exposed to saline or DEP (35 µL at 1 mg/mL) twice weekly for 5 weeks by pulmonary administration. The pan-macrophage marker CD64, CD206 and IRF5 were quantified in paraffin-embedded sections of brachiocephalic artery lesions by immunohistochemistry. Results In M-CSF-differentiated murine macrophages incubated with DEP, significant fold increases in gene expression (relative to PBS control) were observed: IRF5 (1.4-fold), IL-6 (31.5-fold) and IL-12α (5.8-fold) while CD206 (0.8-fold) decreased (Figure 1a, n = 5, p<0.05, two-tailed paired t-tests). CD11c expression was unchanged. No statistically significant differences were detected in GM-CSF-differentiated macrophages. In the brachiocephalic arteries of DEP-exposed ApoE-/- mice we observed increases in CD64+ macrophages (28.2±7.4 vs 15.5±5.6) and IRF5 (16.6±3.6 vs 10.1±3.4) expression, while CD206 expression decreased (4.1±0.9 vs 11.0±1.2) in comparison to saline-exposed mice (Figure 1b mean±SD, n = 5, p<0.05, two-tailed unpaired t-tests, data expressed as % of intima area). Conclusion DEP promotes CD64+ macrophages and IRF5 expression in murine atherosclerosis, while inflammation and IRF5 is induced in vitro, but only in M-CSF-differentiated murine BMDMs. This suggests that DEP has no impact on inflammatory macrophages but could switch homeostatic resident macrophages to a more inflammatory phenotype via the activation of IRF5. Therefore, IRF5 is a promising candidate for further investigation into the initial macrophage response to inhaled particles that leads to the exacerbation of vascular disease.

Association of cardiovascular risk factors and intraplaque neovascularization in symptomatic carotid plaque.

Cardiovascular risk factors are known to contribute to the formation of atherosclerotic plaques, which can result in carotid stenosis. However, the extent to which these factors are associated with intraplaque neovascularization, a key indicator of plaque vulnerability, remains unclear. To investigate this relationship, a study was conducted utilizing contrast-enhanced ultrasound (CEUS) to assess intraplaque neovascularization in symptomatic patients. A cohort of 157 symptomatic patients underwent evaluation using Contrast-Enhanced Ultrasound (CEUS) imaging to assess carotid intraplaque neovascularization, which was quantified based on the degree of plaque enhancement. The collected data encompassed baseline patient characteristics, results from biochemical examinations, cardiovascular risk factors, and medication usage history. Regression analyses were conducted to elucidate the relationship between carotid plaque neovascularization and various cardiovascular risk factors. Patients with intraplaque neovascularization were more prone to have diabetes mellitus (OR 3.81, 95% CI 1.94-7.46, p < 0.001), dyslipidemia (OR 2.36, 95% CI 1.22-4.55, p = 0.011) and hypertension (OR 2.92, 95% CI 1.50-5.71, p = 0.002). Smoking increased the risk of having intraplaque neovascularization (OR 2.25, 95% CI 1.12-4.54, p = 0.023). Treatment with statins was significantly lower in patients with intraplaque neovascularization (OR 0.37, 95% CI 0.19-0.72, p = 0.003). In the multivariate analysis, diabetes mellitus (OR 3.27, 95% CI 1.10-9.78, p = 0.034) was independently related to the presence of intraplaque neovascularization. Meanwhile, compared to the patients in the first tertile of serum glucose (< 6.20 mmol/L), the patients in the third tertile (> 13.35 mmol/L) had the most significance of intraplaque neovascularization (OR 5.55, 95% CI 1.85-16.66, p = 0.002). The findings indicated that diabetes mellitus is a significant cardiovascular risk factor that is strongly associated with carotid intraplaque neovascularization.

Carotid atherosclerotic plaque microcalcification is independently associated with recurrent neurovascular events: A pilot study.

Microcalcification and macrocalcification are critical processes in atherosclerotic plaque progression, though how these processes relate to the risk of stroke recurrence in symptomatic carotid atherosclerosis is poorly understood. We performed a post hoc analysis of data from the ICARUSS (Imaging Carotid Atherosclerosis in the Recovery and Understanding of Stroke Severity) study, where individuals with acute ischemic stroke originating from ipsilateral carotid stenosis of ⩾ 50% underwent 18F-sodium fluoride positron emission tomography (NaF-PET) to measure microcalcification. Tracer uptake was quantified using maximum tissue-to-background ratio (TBRmax). Macrocalcification was measured on computed tomography (CT) using Agatston scoring. Patients were followed up for 6 months for recurrent ipsilateral neurovascular events. Five (27.8%) of 18 individuals had a recurrent ischemic stroke or transient ischemic attack. Ipsilateral carotid plaque NaF uptake at baseline was higher in those with recurrent events compared to those without, and this association remained after adjustment for other vascular risk factors (adjusted odds ratio (aOR) = 1.24, 1.03-1.50). Macrocalcification score in the symptomatic artery was also significantly independently associated with ipsilateral recurrence, but the effect size was relatively smaller (aOR = 1.12, 1.06-1.17 for each 100 unit increase). Our findings indicate that microcalcification in symptomatic carotid plaques is independently associated with ipsilateral ischemic stroke recurrence. Furthermore, differences in the extent of active microcalcification in macrocalcified plaques may help explain variation in the relationship between calcified carotid plaques and stroke recurrence reported in the literature. Our pilot study indicates that evaluation of carotid artery microcalcification using NaF-PET may be a useful method for risk-stratification of carotid atherosclerosis, though our findings require confirmation in larger cohorts.

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

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. 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. 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). 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.

Carotid plaque volume measurement in ischemic stroke: A pilot study

Objectives: Carotid atherosclerosis is a significant risk factor for acute ischemic stroke. Three-dimensional (3D) sonography is a new technique that can be used to analyze carotid plaque both quantitatively and qualitatively. The aim was to study carotid atherosclerosis in stroke patients and healthy controls in terms of plaque volume quantification by 3D ultrasound (US). Materials and Methods: An observational descriptive study was conducted in the stroke unit of a university referral hospital in South India. Patients with ischemic stroke between the ages of 20 and 70 years were studied, along with age- and sex-matched controls. Carotid sonography (2D and 3D) along with Doppler studies was done in all patients, using Philips Affiniti 50 US system. Vascular plaque quantification software was used to assess plaque volume. Results: Twenty-four subjects were recruited, and two were excluded from the study. Twelve were cases, and ten were controls. The mean carotid intima-media thickness (CIMT) (average CIMT in six sites in common carotid artery, three on each side) studied in patients was 0.65 ± 0.10 mm, while that for healthy subjects was 0.62 ± 0.06 mm. The mean plaque volume in patients with stroke or transient ischemic attack was 179.82 ± 310.3 mm3, and that in healthy subjects was 56.75 ± 69.6 mm3. Plaque heterogeneity, surface irregularity, and ulceration were found to be common in symptomatic carotid plaques. Conclusion: The 3D sonography is a non-invasive and simple feasible tool for the analysis of carotid plaque.

Single-Cell Gene-Regulatory Networks of Advanced Symptomatic Atherosclerosis.

While our understanding of the single-cell gene expression patterns underlying the transformation of vascular cell types during the progression of atherosclerosis is rapidly improving, the clinical and pathophysiological relevance of these changes remains poorly understood. Single-cell RNA sequencing data generated with SmartSeq2 (≈8000 genes/cell) in 16 588 single cells isolated during atherosclerosis progression in Ldlr-/-Apob100/100 mice with human-like plasma lipoproteins and from humans with asymptomatic and symptomatic carotid plaques was clustered into multiple subtypes. For clinical and pathophysiological context, the advanced-stage and symptomatic subtype clusters were integrated with 135 tissue-specific (atherosclerotic aortic wall, mammary artery, liver, skeletal muscle, and visceral and subcutaneous, fat) gene-regulatory networks (GRNs) inferred from 600 coronary artery disease patients in the STARNET (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task) study. Advanced stages of atherosclerosis progression and symptomatic carotid plaques were largely characterized by 3 smooth muscle cells (SMCs), and 3 macrophage subtype clusters with extracellular matrix organization/osteogenic (SMC), and M1-type proinflammatory/Trem2-high lipid-associated (macrophage) phenotypes. Integrative analysis of these 6 clusters with STARNET revealed significant enrichments of 3 arterial wall GRNs: GRN33 (macrophage), GRN39 (SMC), and GRN122 (macrophage) with major contributions to coronary artery disease heritability and strong associations with clinical scores of coronary atherosclerosis severity. The presence and pathophysiological relevance of GRN39 were verified in 5 independent RNAseq data sets obtained from the human coronary and aortic artery, and primary SMCs and by targeting its top-key drivers, FRZB and ALCAM in cultured human coronary artery SMCs. By identifying and integrating the most gene-rich single-cell subclusters of atherosclerosis to date with a coronary artery disease framework of GRNs, GRN39 was identified and independently validated as being critical for the transformation of contractile SMCs into an osteogenic phenotype promoting advanced, symptomatic atherosclerosis.

Molecular Pathways of Vulnerable Carotid Plaques at Risk of Ischemic Stroke: A Narrative Review.

The concept of vulnerable carotid plaques is pivotal in understanding the pathophysiology of ischemic stroke secondary to large-artery atherosclerosis. In macroscopic evaluation, vulnerable plaques are characterized by one or more of the following features: microcalcification; neovascularization; lipid-rich necrotic cores (LRNCs); intraplaque hemorrhage (IPH); thin fibrous caps; plaque surface ulceration; huge dimensions, suggesting stenosis; and plaque rupture. Recognizing these macroscopic characteristics is crucial for estimating the risk of cerebrovascular events, also in the case of non-significant (less than 50%) stenosis. Inflammatory biomarkers, such as cytokines and adhesion molecules, lipid-related markers like oxidized low-density lipoprotein (LDL), and proteolytic enzymes capable of degrading extracellular matrix components are among the key molecules that are scrutinized for their associative roles in plaque instability. Through their quantification and evaluation, these biomarkers reveal intricate molecular cross-talk governing plaque inflammation, rupture potential, and thrombogenicity. The current evidence demonstrates that plaque vulnerability phenotypes are multiple and heterogeneous and are associated with many highly complex molecular pathways that determine the activation of an immune-mediated cascade that culminates in thromboinflammation. This narrative review provides a comprehensive analysis of the current knowledge on molecular biomarkers expressed by symptomatic carotid plaques. It explores the association of these biomarkers with the structural and compositional attributes that characterize vulnerable plaques.

Improving radiomic modeling for the identification of symptomatic carotid atherosclerotic plaques using deep learning-based 3D super-resolution CT angiography

Rationale and objectivesRadiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. Materials and methodsA total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups. SR CTA images were derived from NR CTA images using deep learning-based three-dimensional SR technology. Handcrafted radiomic features were extracted from both the SR and NR CTA images and three risk models were developed based on manually measured quantitative CTA characteristics and NR and SR radiomic features. Model performances were assessed via receiver operating characteristic, calibration, and decision curve analyses. ResultsThe SR model exhibited the optimal performance (area under the curve [AUC] 0.820, accuracy 0.802, sensitivity 0.854, F1 score 0.847) in the testing cohort, outperforming the other two models. The calibration curve analyses and Hosmer–Lemeshow test demonstrated that the SR model exhibited the best goodness of fit, and decision curve analysis revealed that SR model had the highest clinical value and potential patient benefits. ConclusionsDeep learning-based three-dimensional SR technology could improve the CTA-based radiomic models in identifying symptomatic carotid plaques, potentially providing more accurate and valuable information to guide clinical decision-making to reduce the risk of ischemic stroke.

Abstract 102: Microanatomy of Intracranial Human Thrombi by Single Nuclei Transcriptomics

Introduction: Microvascular dysfunction and inflammation are thought to underlie reperfusion failure following endovascular thrombectomy. Here we present the first study to produce a transcriptome-based cellular landscape of human intracranial thrombi highlighting the importance of cellular plasticity and intercellular communication at the site of occlusion. Methods: We performed single nuclei RNA-sequencing (snRNA-seq) on intracranial human thrombi harvested during revascularization. Unsupervised clustering, data visualization, differential expression and functional gene ontology/pathway analysis were utilized to deconvolve expression data into cell type-specific expression profiles. Thrombi snRNA-seq data were integrated with single cell RNA-seq data from symptomatic and asymptomatic human carotid plaques and healthy brain vasculature to contextualize thrombi in atherosclerotic disease, perform diffusion coefficient analysis, and identify ligand-receptor interactions. Results: Unbiased clustering identified six cellular populations in distinct effector states within the thrombus microenvironment. Functional gene ontology and pathway analysis revealed a monocyte subpopulation in a distinct endothelial-activation and neuroplasticity state, supporting the notion of cellular trans-differentiation and interconversion. Integration with carotid plaque data and diffusion coefficient analysis revealed shared transcriptional programs existing in a continuum, progressing from asymptomatic plaque to embolized thrombus. Ligand-receptor analysis identified macrophage mediated T-cell activation, pro-inflammatory response, immunological synapse stabilization, neuroplasticity, and monocyte trans-endothelial migration. Conclusion: Our novel application of snRNA-seq to thrombectomy specimens provides the highest resolution exploration of cellular differences and a best understanding of individual contextualized cell function to date. This detailed cellular landscape will facilitate mapping novel interventional targets with direct functional relevance and allow for greater personalization of post-reperfusion therapy.

CT-Diagnosed Non-Alcoholic Fatty Liver Disease as a Risk Predictor of Symptomatic Carotid Plaque and Cerebrovascular Symptoms.

We aimed to test whether computed tomography (CT)-diagnosed Non-Alcoholic Fatty Liver Disease (NAFLD) is a risk factor for cerebrovascular symptoms in patients with suspected atherosclerotic disease. A total of 550 patients (mean age 65.2 ± 8.8years, 370 males) with carotid plaques who underwent carotid computed tomographic angiography (CTA) and unenhanced abdominal CT were retrospectively analyzed. NAFLD was diagnosed by abdominal CT. Carotid CTA assessed the presence of carotid artery stenosis or plaque. The relationship between NAFLD and cerebrovascular symptoms was analyzed using generalized estimating equations and receiver operating characteristic (ROC) analysis. The prevalence of NAFLD was significantly higher in symptomatic patients (76.5 vs 9.8%; P < .001). After adjusting for several confounding factors (e.g., hypertension and hyperlipidemia), univariate and multivariate logic regression analysis revealed that NAFLD was still strongly associated with cerebrovascular symptoms (odds ratio, 22.81; 95% CI 13.03-39.93; P < .001). ROC analysis showed that the area under the curve for discriminating symptomatic and asymptomatic plaques using NAFLD measurements was 0.833, with a sensitivity of 76.5% and a specificity of 90.2%. NAFLD is strongly associated with an increased risk of cerebrovascular symptoms. It may be an important predictor of symptomatic carotid plaque and cerebrovascular symptoms.

Identification of symptomatic carotid artery plaque: a predictive model combining angiography with optical coherence tomography.

Symptomatic carotid artery disease is indicative of an elevated likelihood of experiencing a subsequent stroke, with the morphology of plaque and its specific features being closely linked to the risk of stroke occurrence. Our study based on the characteristics of carotid plaque assessed by optical coherence tomography (OCT), the plaque morphology evaluated by digital subtraction angiography (DSA) and clinical laboratory indicators were combined, develop a combined predictive model to identify symptomatic carotid plaque. Patients diagnosed with carotid atherosclerotic stenosis who underwent whole-brain DSA and OCT examination at the Affiliated Hospital of Jining Medical University from January 2021 to November 2023 were evaluated. Clinical features, as well as DSA and OCT plaque characteristics, were analyzed for differences between symptomatic and asymptomatic cohorts. An analysis of logistic regression was carried out to identify factors associated with the presence of symptomatic carotid plaque. A multivariate binary logistic regression equation was established with the odds ratio (OR) serving as the risk assessment parameter. The receiver operating characteristic curve was utilized to assess the combined predictive model and independent influencing factors. A total of 52 patients were included in the study (symptomatic: 44.2%, asymptomatic: 55.8%). Symptomatic carotid stenosis was significantly linked to four main factors: low-density lipoprotein-cholesterol >3.36 mmol/L [OR, 6.400; 95% confidence interval (CI), 1.067-38.402; p = 0.042], irregular plaque (OR, 6.054; 95% CI, 1.016-36.083; p = 0.048), ruptured plaque (OR, 6.077; 95% CI, 1.046-35.298; p = 0.048), and thrombus (OR, 6.773; 95% CI, 1.194-38.433; p = 0.044). The combined predictive model generated using four indicators showed good discrimination (Area Under Curve, 0.924; 95% CI, 0.815-0. 979). The p value was <0.05 with 78.26% sensitivity and 93.10% specificity. OCT is valuable in evaluating the plaque characteristics of carotid atherosclerotic stenosis. The combined predictive model comprising low-density lipoprotein-cholesterol >3.36 mmol/L, irregular plaque, ruptured plaque, and thrombus could help in the detection of symptomatic carotid plaque. Further research conducted on additional independent cohorts is necessary to confirm the clinical significance of the predictive model for symptomatic carotid plaque.

Signal intensity and volume of carotid intraplaque hemorrhage on magnetic resonance imaging and the risk of ipsilateral cerebrovascular events: The Plaque At RISK (PARISK) study

BackgroundThe Plaque at RISK (PARISK) study demonstrated that patients with a carotid plaque with intraplaque hemorrhage (IPH) have an increased risk of recurrent ipsilateral ischemic cerebrovascular events. It was previously reported that symptomatic carotid plaques with IPH showed higher IPH signal intensity ratios (SIR) and larger IPH volumes than asymptomatic plaques. We explored whether IPH SIR and IPH volume are associated with future ipsilateral ischemic cerebrovascular events beyond the presence of IPH. MethodsTIA and ischemic stroke patients with mild-to-moderate carotid stenosis and an ipsilateral IPH-positive carotid plaque (n=89) from the PARISK study were included. The clinical endpoint was a new ipsilateral ischemic cerebrovascular event during 5 years of follow-up, while the imaging-based endpoint was a new ipsilateral brain infarct on brain MRI after 2 years (n=69). Trained observers delineated IPH, a hyperintense region compared to surrounding muscle tissue on hyper T1-weighted MR images. The IPH SIR was the maximal signal intensity in the IPH region divided by the mean signal intensity of adjacent muscle tissue. The associations between IPH SIR or volume and the clinical and imaging-based endpoint were investigated using Cox-proportional hazard models and logistic regression, respectively. ResultsDuring 5.1 (interquartile range (IQR): 3.1-5.6) years of follow-up, 21 ipsilateral cerebrovascular ischemic events were identified. Twelve new ipsilateral brain infarcts were identified on the 2-year neuro MRI. There was no association for IPH SIR or IPH volume with the clinical endpoint (HR: 0.89 [95% CI: 0.67-1.10] and HR: 0.91 [0.69-1.19] per 100µl increase, respectively) nor with the imaging-based endpoint (OR: 1.04 [0.75-1.45] and OR: 1.21 [0.87-1.68] per 100µl increase, respectively). ConclusionsIPH SIR and IPH volume were not associated with future ipsilateral ischemic cerebrovascular events. Therefore, quantitative assessment of IPH does not seem to provide additional value beyond the presence of IPH for stroke risk assessment.Trial registrationThe PARISK study was registered on ClinicalTrials.gov with ID NCT01208025 on 21 September 2010 (https://clinicaltrials.gov/study/NCT01208025).

Computed tomography angiography-based radiomics model to identify high-risk carotid plaques.

Extracranial atherosclerosis is one of the major causes of stroke. Carotid computed tomography angiography (CTA) is a widely used imaging modality that allows detailed assessments of plaque characteristics. This study aimed to develop and test radiomics models of carotid plaques and perivascular adipose tissue (PVAT) to distinguish symptomatic from asymptomatic plaques and compare the diagnostic value between radiomics models and traditional CTA model. A total of 144 patients with carotid plaques were divided into symptomatic and asymptomatic groups. The traditional CTA model was built by the traditional radiological features of carotid plaques measured on CTA images which were screened by univariate analysis and multivariable logistic regression. We extracted and screened radiomics features from carotid plaques and PVAT. Then, a support vector machine was used for building plaque and PVAT radiomics models, as well as a combined model using traditional CTA features and radiomics features. The diagnostic value between radiomics models and traditional CTA model was compared in identifying symptomatic carotid plaques by Delong method. The area under curve (AUC) values of traditional CTA model were 0.624 and 0.624 for the training and validation groups, respectively. The plaque radiomics model and PVAT radiomics model achieved AUC values of 0.766, 0.740 and 0.759, 0.618 in the two groups, respectively. Meanwhile, the combined model of plaque and PVAT radiomics features and traditional CTA features had AUC values of 0.883 and 0.840 for the training and validation groups, respectively, and the receiver operating characteristic curves of combined model were significantly better than those of traditional CTA model in the training group (P<0.001) and validation group (P=0.029). The combined model of the radiomics features of carotid plaques and PVAT and the traditional CTA features significantly contributes to identifying high-risk carotid plaques compared with traditional CTA model.

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.

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques.

In this study, we aimed to investigate the classification of symptomatic plaques by evaluating the models generated via two different approaches, a radiomics-based machine learning (ML) approach, and an end-to-end learning approach which utilized deep learning (DL) techniques with several representative model frameworks. We collected high-resolution magnetic resonance imaging (HRMRI) data from 104 patients with carotid artery stenosis, who were diagnosed with either symptomatic plaques (SPs) or asymptomatic plaques (ASPs), in two medical centers. 74 patients were diagnosed with SPs and 30 patients were ASPs. Sampling Perfection with Application-optimized Contrasts (SPACE) by using different flip angle Evolutions was used for MRI imaging. Repeated stratified five-fold cross-validation was used to evaluate the accuracy and receiver operating characteristic (ROC) of the trained classifier. The two proposed approaches were investigated to train the models separately. The difference in the model performance of the two proposed methods was quantitatively evaluated to find a better model to differentiate between SPs and ASPs. 3D-SE-Densenet-121 model showed the best performance among all prediction models (AUC, accuracy, precision, sensitivity, and F1-score of 0.9300, 0.9308, 0.9008, 0.8588, and 0.8614, respectively), which were 0.0689, 0.1119, 0.1043, 0.0805, and 0.1089 higher than the best radiomics-based ML model (MLP). Decision curve analysis showed that the 3D-SE-Densenet-121 model delivered more net benefit than the best radiomics-based ML model (MLP) with a wider threshold probability. The DL models were able to accurately differentiate between symptomatic and asymptomatic carotid plaques with limited data, which outperformed radiomics-based ML models in identifying symptomatic plaques.

Machine learning-based identification of symptomatic carotid atherosclerotic plaques with dual-energy computed tomography angiography

ObjectiveThis study aimed to develop and validate a machine learning model incorporating both dual-energy computed tomography (DECT) angiography quantitative parameters and clinically relevant risk factors for the identification of symptomatic carotid plaques to prevent acute cerebrovascular events. MethodsThe data of 180 patients with carotid atherosclerosis plaques were analysed from January 2017 to December 2021; 110 patients (64.03±9.58 years old, 20 women, 90 men) were allocated to the symptomatic group, and 70 patients (64.70±9.89 years old, 50 women, 20 men) were allocated to the asymptomatic group. Overall, five machine learning models using the XGBoost algorithm, based on different CT and clinical features, were developed in the training cohort. The performances of all five models were assessed in the testing cohort using receiver operating characteristic curves, accuracy, recall rate, and F1 score. ResultsThe shapley additive explanation (SHAP) value ranking showed fat fraction (FF) as the highest among all CT and clinical features and normalised iodine density (NID) as the 10th. The model based on the top 10 features from the SHAP measurement showed optimal performance (area under the curve [AUC] .885, accuracy .833, recall rate .933, F1 score .861), compared with the other four models based on conventional CT features (AUC .588, accuracy .593, recall rate .767, F1 score .676), DECT features (AUC .685, accuracy .648, recall rate .667, F1 score .678), conventional CT and DECT features (AUC .819, accuracy .740, recall rate .867, F1 score .788), and all CT and clinical features (AUC .878, accuracy .833, recall rate .867, F1 score .852). ConclusionFF and NID can serve as useful imaging markers of symptomatic carotid plaques. This tree-based machine learning model incorporating both DECT and clinical features could potentially comprise a non-invasive method for identification of symptomatic carotid plaques to guide clinical treatment strategies.

Comparative Analysis of Symptomatic and Asymptomatic Carotid Plaque With Bilateral Mild Carotid Stenosis (30%-50%).

HomeStrokeAhead of PrintComparative Analysis of Symptomatic and Asymptomatic Carotid Plaque With Bilateral Mild Carotid Stenosis (30%–50%) No AccessResearch ArticleRequest AccessAboutView PDFSections ToolsAdd to favoritesDownload citationsTrack citationsPermissionsDownload Articles + Supplements ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toSupplemental MaterialNo AccessResearch ArticleRequest AccessComparative Analysis of Symptomatic and Asymptomatic Carotid Plaque With Bilateral Mild Carotid Stenosis (30%–50%) Atulabh Vajpeyee, Pushan Sharma, Piyush Kumar Anshu, Shivam Tiwari, Lokendra Bahadur Yadav, Kapil Vyas and Manisha Vajpeyee Atulabh VajpeyeeAtulabh Vajpeyee Correspondence to: Atulabh Vajpeyee, MD, DM (Neurology), FINS, Department of Neurosciences, Pacific Medical College and Hospital, Pacific Medical University, Udaipur, India313001. Email E-mail Address: [email protected] https://orcid.org/0000-0002-7942-4302 Department of Neurosciences, Pacific Medical College and Hospital, Pacific Medical University, Udaipur, Rajasthan, India (A.V., P.S., P.K.A., S.T., L.B.Y.). *A. Vajpeyee, S. Tiwari, and L.B. Yadav contributed equally. Search for more papers by this author , Pushan SharmaPushan Sharma https://orcid.org/0000-0002-4965-6655 Department of Neurosciences, Pacific Medical College and Hospital, Pacific Medical University, Udaipur, Rajasthan, India (A.V., P.S., P.K.A., S.T., L.B.Y.). Search for more papers by this author , Piyush Kumar AnshuPiyush Kumar Anshu https://orcid.org/0000-0003-1606-9208 Department of Neurosciences, Pacific Medical College and Hospital, Pacific Medical University, Udaipur, Rajasthan, India (A.V., P.S., P.K.A., S.T., L.B.Y.). Search for more papers by this author , Shivam TiwariShivam Tiwari https://orcid.org/0000-0001-9465-1215 Department of Neurosciences, Pacific Medical College and Hospital, Pacific Medical University, Udaipur, Rajasthan, India (A.V., P.S., P.K.A., S.T., L.B.Y.). *A. Vajpeyee, S. Tiwari, and L.B. Yadav contributed equally. Search for more papers by this author , Lokendra Bahadur YadavLokendra Bahadur Yadav https://orcid.org/0000-0002-5035-0573 Department of Neurosciences, Pacific Medical College and Hospital, Pacific Medical University, Udaipur, Rajasthan, India (A.V., P.S., P.K.A., S.T., L.B.Y.). *A. Vajpeyee, S. Tiwari, and L.B. Yadav contributed equally. Search for more papers by this author , Kapil VyasKapil Vyas Department of Radiology, Pacific Medical College and Hospital, Pacific Medical University, Udaipur, Rajasthan, India.(K.V.) Search for more papers by this author and Manisha VajpeyeeManisha Vajpeyee https://orcid.org/0000-0001-5010-2863 Department of Reproductive Medicine and Research, Pacific Medical College and Hospital, Pacific Medical University, Udaipur, Rajasthan, India. (M.V.) Search for more papers by this author Originally published2 Jun 2023https://doi.org/10.1161/STROKEAHA.123.042874Stroke. 2023;0"Comparative Analysis of Symptomatic and Asymptomatic Carotid Plaque With Bilateral Mild Carotid Stenosis (30%–50%)." Stroke, , pp. FootnotesFor Sources of Funding and Disclosures, see page XXX.Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/STROKEAHA.123.042874.*A. Vajpeyee, S. Tiwari, and L.B. Yadav contributed equally.Correspondence to: Atulabh Vajpeyee, MD, DM (Neurology), FINS, Department of Neurosciences, Pacific Medical College and Hospital, Pacific Medical University, Udaipur, India313001. Email [email protected]com Previous Back to top Next FiguresReferencesRelatedDetails Advertisement Article InformationMetrics © 2023 American Heart Association, Inc.https://doi.org/10.1161/STROKEAHA.123.042874PMID: 37264912 Originally publishedJune 2, 2023 Keywordscarotid arterieshemorrhagefibrous capmorphologyconsentPDF download Advertisement SubjectsPhysiologyVascular Biology

Transforming growth factor-β2 is associated with atherosclerotic plaque stability and lower risk for cardiovascular events.

Transforming growth factor-beta (TGF-β) exists in three isoforms TGF-β1, -β2 and -β3. TGF-β1 has been suggested to be important for maintaining plaque stability, yet the role of TGF-β2 and -β3 in atherosclerosis remains to be investigated. This study explores the association of these three isoforms of TGF-β with plaque stability in the human atherosclerotic disease. TGF-β1, -β2 and -β3 proteins were quantified in 223 human carotid plaques by immunoassays. Indications for the endarterectomy were: symptomatic carotid plaque with stenosis >70% or without symptoms and >80% stenosis. Plaque mRNA levels were assessed by RNA sequencing. Plaque components and extracellular matrix were measured histologically and biochemically. Matrix metalloproteinases were measured with ELISA. Monocyte chemoattractant protein-1 (MCP-1) was measured with immunoassays. The effect of TGF-β2 on inflammation and protease activity was investigated in vitro using THP-1 and RAW264.7 macrophages. Patients were followed longitudinally for cardiovascular events.TGF-β2 was the most abundant isoform and was increased at both protein and mRNA levels in asymptomatic plaques. TGF-β2 was the main determinant separating asymptomatic plaques in an Orthogonal Projections to Latent Structures Discriminant Analysis. TGF-β2 correlated positively to features of plaque stability and inversely to markers of plaque vulnerability. TGF-β2 was the only isoform inversely correlated to the matrix-degrading matrix metalloproteinase-9 and inflammation in the plaque tissue. In vitro, TGF-β2 pre-treatment reduced MCP-1 gene and protein levels as well as matrix metalloproteinase-9 gene levels and activity. Patients with plaques with high TGF-β2 levels had a lower risk to suffer from future cardiovascular events. TGF-β2 is the most abundant TGF-β isoform in human plaques and may maintain plaque stability by decreasing inflammation and matrix degradation.

Systematic Review on Magnetic Resonance Angiography with Vessel Wall Imaging for the Characterization of Symptomatic Carotid Artery Plaque

To perform a systematic literature review to assess the usefulness of performing magnetic resonance angiography (MRA) with vessel wall imaging (VWI) sequences for the assessment of symptomatic carotid artery plaques and the identification of risky plaque features predisposing for stroke. We performed a systematic review of the literature pertaining to MRA with VWI techniques in patients with carotid artery disease, focusing on symptomatic patients' plaque features and morphology. Independent reviewers screened and analyzed data extracted from eligible studies, and a modified Newcastle-Ottawa Scale was used to appraise the quality of the design and content of the selected manuscripts to achieve an accurate interpretation. This review included nineteen peer-reviewed manuscripts, all of them including MRA and VWI assessments of the symptomatic carotid artery plaque. We focused on patients' comorbidities and reviewed plaque features, including intraplaque hemorrhage, a lipid-rich necrotic core, a ruptured fibrous cap, and plaque ulceration. MRA with VWI is a useful tool in the evaluation of carotid artery plaques. This imaging technique allows clinicians to identify plaques at risk of causing a neurovascular event. The presence of intraplaque hemorrhage, plaque ulceration, a ruptured fibrous cap, and a lipid-rich necrotic core are associated with neurovascular symptoms. The timely identification of these features could have a positive impact on neurovascular event prevention.

399 Role of Plaque Inflammation in Symptomatic Carotid Stenosis

INTRODUCTION: Prior studies have shown that plaque inflammation on FDG-PET and the SCAIL score were associated with recurrent ischemic events. However, the findings have thus far not been widely validated. METHODS: This was a single-center prospective cohort study that included patients with (1) recent TIA or ischemic stroke within the past 30 days, (2) ipsilateral carotid artery stenosis of ≥50%, and (3) not considered for early carotid revascularization. The (1) maximum standardized uptake value (SUVmax) of the symptomatic carotid plaque, (2) stenosis severity of the symptomatic carotid artery, and (3) the SCAIL score (a composite score consisting of the degree of plaque inflammation and stenosis severity) were measured for all patients. The outcomes were (1) a 90-day ipsilateral ischemic stroke and (2) a 90-day ipsilateral symptomatic TIA or major adverse cardiovascular event (MACE). RESULTS: Of 131 included patients, 21 (16.0%) patients had a 90-day ischemic stroke, and 41 (31.3%) patients had a 90-day TIA or MACE. Symptomatic plaque SUVmax (AUC = 0.92; 95% CI = 0.82–1.00) was superior to the SCAIL score (AUC = 0.79; 95% CI = 0.65, 0.92) in predicting 90-day ischemic stroke. Symptomatic plaque SUVmax (AUC = 0.84; 95% CI = 0.76, 0.92) was also superior to the SCAIL score (AUC = 0.76; 95% CI = 0.66, 0.85) in predicting 90-day TIA or MACE. CONCLUSIONS: Plaque inflammation may serve as a reliable biomarker for risk stratification and selection of patients for carotid revascularization and/or anti-inflammatory therapy.