Dataset Information

Identification of High-Risk Plaques by MRI and Fluorescence Imaging in a Rabbit Model of Atherothrombosis.

ABSTRACT:

Introduction

The detection of atherosclerotic plaques at risk for disruption will be greatly enhanced by molecular probes that target vessel wall biomarkers. Here, we test if fluorescently-labeled Activatable Cell Penetrating Peptides (ACPPs) could differentiate stable plaques from vulnerable plaques that disrupt, forming a luminal thrombus. Additionally, we test the efficacy of a combined ACPP and MRI technique for identifying plaques at high risk of rupture.

Methods and results

In an atherothrombotic rabbit model, disrupted plaques were identified with in vivo MRI and co-registered in the same rabbit aorta with the in vivo uptake of ACPPs, cleaved by matrix metalloproteinases (MMPs) or thrombin. ACPP uptake, mapped ex vivo in whole aortas, was higher in disrupted compared to non-disrupted plaques. Specifically, disrupted plaques demonstrated a 4.5~5.0 fold increase in fluorescence enhancement, while non-disrupted plaques showed only a 2.2~2.5 fold signal increase. Receiver operating characteristic (ROC) analysis indicates that both ACPPs (MMP and thrombin) show high specificity (84.2% and 83.2%) and sensitivity (80.0% and 85.7%) in detecting disrupted plaques. The detection power of ACPPs was improved when combined with the MRI derived measure, outward remodeling ratio.

Conclusions

Our targeted fluorescence ACPP probes distinguished disrupted plaques from stable plaques with high sensitivity and specificity. The combination of anatomic, MRI-derived predictors for disruption and ACPP uptake can further improve the power for identification of high-risk plaques and suggests future development of ACPPs with molecular MRI as a readout.

SUBMITTER: Hua N

PROVIDER: S-EPMC4598148 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Identification of High-Risk Plaques by MRI and Fluorescence Imaging in a Rabbit Model of Atherothrombosis.

Hua Ning N Baik Fred F Pham Tuan T Phinikaridou Alkystis A Giordano Nick N Friedman Beth B Whitney Michael M Nguyen Quyen T QT Tsien Roger Y RY Hamilton James A JA

PloS one 20151008 10

<h4>Introduction</h4>The detection of atherosclerotic plaques at risk for disruption will be greatly enhanced by molecular probes that target vessel wall biomarkers. Here, we test if fluorescently-labeled Activatable Cell Penetrating Peptides (ACPPs) could differentiate stable plaques from vulnerable plaques that disrupt, forming a luminal thrombus. Additionally, we test the efficacy of a combined ACPP and MRI technique for identifying plaques at high risk of rupture.<h4>Methods and results</h4> ...[more]

PMID: 26448434

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Project description:BackgroundExtracranial 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.MethodsA 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.ResultsThe 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).ConclusionsThe 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.

Dataset Information

Identification of High-Risk Plaques by MRI and Fluorescence Imaging in a Rabbit Model of Atherothrombosis.

Introduction

Methods and results

Conclusions

Publications

Identification of High-Risk Plaques by MRI and Fluorescence Imaging in a Rabbit Model of Atherothrombosis.

Similar Datasets

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