Dataset Information

Innovative Multiparametric Characterization of Carotid Plaque Vulnerability by Ultrasound.

ABSTRACT: Objective:The degree of stenosis of a carotid plaque is a well-established risk factor for ischemic stroke. Nevertheless, the risk of ipsilateral stroke in asymptomatic carotid stenosis remains low and new imaging markers are needed to better target which patients would benefit most from endarterectomy or intensive medical therapy. Ultrafast ultrasound imaging offers parameters helping at characterizing the carotid plaque by shear wave elastography and Ultrafast Doppler (UFD). We aimed at using these techniques to characterize 3 different ultrasound biomarkers: plaque stiffness heterogeneity, wall shear stress (WSS) and intraplaque micro-flows and to correlate these biomarkers with findings on computed tomography angiography (CTA) and the pathological examination. Methods:We present the case of a multimodal evaluation of a carotid plaque using ultrasound. Elastography has been coupled to the WSS assessment and the detection of intraplaque micro-flows by UFD. The data have been compared to CTA and to the pathology examination of the tissue after carotid endarterectomy. Results:Elastography allowed at identifying stiff areas corresponding to calcifications, as well as a soft area corresponding to an intraplaque hemorrhage. The flow evaluation with UFD showed an increase of the WSS along the plaque and identified the presence of a plaque rupture, confirmed by the pathologist. Conclusion:Ultrafast ultrasound imaging is an innovative, easily accessible technique that provides imaging modalities on top of the conventional B-mode. Ultrafast ultrasound biomarkers such as plaque stiffness heterogeneity, WSS and intraplaque micro-flows could help to define the vulnerability of the carotid plaque in order to stratify patients that could benefit most from endarterectomy or intensive medical therapy.

SUBMITTER: Goudot G

PROVIDER: S-EPMC7064056 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Innovative Multiparametric Characterization of Carotid Plaque Vulnerability by Ultrasound.

Goudot Guillaume G Khider Lina L Pedreira Olivier O Poree Jonathan J Julia Pierre P Alsac Jean-Marc JM Amemiya Kisaki K Bruneval Patrick P Messas Emmanuel E Pernot Mathieu M Mirault Tristan T

Frontiers in physiology 20200303

<h4>Objective</h4>The degree of stenosis of a carotid plaque is a well-established risk factor for ischemic stroke. Nevertheless, the risk of ipsilateral stroke in asymptomatic carotid stenosis remains low and new imaging markers are needed to better target which patients would benefit most from endarterectomy or intensive medical therapy. Ultrafast ultrasound imaging offers parameters helping at characterizing the carotid plaque by shear wave elastography and Ultrafast Doppler (UFD). We aimed a ...[more]

PMID: 32194437

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Project description:BackgroundIL-6 (interleukin-6) has important roles in atherosclerosis pathophysiology. To determine if anti-IL-6 therapy warrants evaluation as an adjuvant stroke prevention strategy in patients with carotid atherosclerosis, we tested whether circulating IL-6 levels predict carotid plaque severity, vulnerability, and progression in the prospective population-based CHS (Cardiovascular Health Study).MethodsDuplex carotid ultrasound was performed at baseline and 5 years. Baseline plaque severity was scored 0 to 5 based on North American Symptomatic Carotid Endarterectomy Trial grade of stenosis. Plaque vulnerability at baseline was the presence of markedly irregular, ulcerated, or echolucent plaques. Plaque progression at 5 years was a ≥1 point increase in stenosis severity. The relationship of baseline plasma IL-6 levels with plaque characteristics was modeled using multivariable linear (severity) or logistic (vulnerability and progression) regression. Risk factors of atherosclerosis were included as independent variables. Stepwise backward elimination was used with P>0.05 for variable removal. To assess model stability, we computed the E-value or minimum strength of association (odds ratio scale) that unmeasured confounders must have with log IL-6 and the outcome to suppress the association. We performed internal validation with 100 bootstrap samples.ResultsThere were 4334 participants with complete data (58.9% women, mean age: 72.7±5.1 years), including 1267 (29.2%) with vulnerable plaque and 1474 (34.0%) with plaque progression. Log IL-6 predicted plaque severity (β=0.09, P=1.3×10-3), vulnerability (OR, 1.21 [95% CI, 1.05-1.40]; P=7.4×10-3, E-value=1.71), and progression (OR, 1.44 [95% CI, 1.23-1.69], P=9.1×10-6, E-value 2.24). In participants with >50% predicted probability of progression, mean log IL-6 was 0.54 corresponding to 2.0 pg/mL. Dichotomizing IL-6 levels did not affect the performance of prediction models.ConclusionsCirculating IL-6 predicts carotid plaque severity, vulnerability, and progression. The 2.0 pg/mL cutoff could facilitate the selection of individuals that would benefit from anti-IL-6 drugs for stroke prevention.

Project description:Vulnerable carotid atherosclerotic plaque (CAP) significantly contributes to ischemic stroke. Neovascularization within plaques is an emerging biomarker linked to plaque vulnerability that can be detected using contrast-enhanced ultrasound (CEUS). Computed tomography angiography (CTA) is a common method used in clinical cerebrovascular assessments that can be employed to evaluate the vulnerability of CAPs. Radiomics is a technique that automatically extracts radiomic features from images. This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package was used to extract radiomic features in Python. Machine learning algorithms containing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perception (MLP) were used to construct the models. The confusion matrix, receiver operating characteristic (ROC) curve, accuracy, precision, recall, and f-1 score were used to evaluate the performance of the models. A total of 74 patients with 110 CAPs were included. In all, 1,316 radiomic features were extracted, and 10 radiomic features were selected for machine-learning model construction. After evaluating several models on the testing cohorts, it was discovered that model_RF outperformed the others, achieving an AUC value of 0.93 (95% CI: 0.88-0.99). The accuracy, precision, recall, and f-1 score of model_RF in the testing cohort were 0.85, 0.87, 0.85, and 0.85, respectively. Radiomic features associated with the neovascularization of CAP were obtained. Our study highlights the potential of radiomics-based models for improving the accuracy and efficiency of diagnosing vulnerable CAP. In particular, the model_RF, utilizing radiomic features extracted from CTA, provides a noninvasive and efficient method for accurately predicting the vulnerability status of CAP. This model shows great potential for offering clinical guidance for early detection and improving patient outcomes.

Dataset Information

Innovative Multiparametric Characterization of Carotid Plaque Vulnerability by Ultrasound.

Publications

Innovative Multiparametric Characterization of Carotid Plaque Vulnerability by Ultrasound.

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