Dataset Information

Assessment of atherosclerotic carotid plaque volume with multidetector computed tomography angiography.

ABSTRACT: PURPOSE: The amount of atherosclerotic plaque and its components (calcifications, fibrous tissue, and lipid core) could be better predictors of acute events than the now currently used degree of stenosis. Therefore, we evaluated a dedicated software tool for volume measurements of atherosclerotic carotid plaque and its components in multidetector computed tomography angiography (MDCTA) images. MATERIALS AND METHODS: Data acquisition was approved by the Institutional Review Board and all patients gave written informed consent. MDCTA images of 56 carotid arteries were analyzed by three observers. Plaque volumes were assessed by manual drawing of the outer vessel contour. The luminal boundary was determined based on a Hounsfield-Unit (HU) threshold. The contribution of different components was measured by the number of voxels within defined ranges of HU-values (calcification >130 HU, fibrous tissue 60-130 HU, lipid core <60 HU). Interobserver variability (IOV) was assessed. RESULTS: Plaque volume was 1,259 +/- 621 mm3. The calcified, fibrous and lipid volumes were 238 +/- 252 mm3, 647 +/- 277 mm3 and 376 +/- 283 mm3, respectively. IOV was moderate with interclass correlation coefficients (ICC) ranging from 0.76 to 0.99 and coefficients of variation (COV) ranging from 3% to 47%. CONCLUSION: Atherosclerotic carotid plaque volume and plaque component volumes can be assessed with MDCTA with a reasonable observer variability.

SUBMITTER: de Weert TT

PROVIDER: S-EPMC2522292 | biostudies-other | 2008 Oct

REPOSITORIES: biostudies-other

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Assessment of atherosclerotic carotid plaque volume with multidetector computed tomography angiography.

de Weert Thomas T TT de Monyé Cécile C Meijering Erik E Booij Ronald R Niessen Wiro J WJ Dippel Diederik W J DW van der Lugt Aad A

The international journal of cardiovascular imaging 20080329 7

<h4>Purpose</h4>The amount of atherosclerotic plaque and its components (calcifications, fibrous tissue, and lipid core) could be better predictors of acute events than the now currently used degree of stenosis. Therefore, we evaluated a dedicated software tool for volume measurements of atherosclerotic carotid plaque and its components in multidetector computed tomography angiography (MDCTA) images.<h4>Materials and methods</h4>Data acquisition was approved by the Institutional Review Board and ...[more]

PMID: 18373211

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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

Assessment of atherosclerotic carotid plaque volume with multidetector computed tomography angiography.

Publications

Assessment of atherosclerotic carotid plaque volume with multidetector computed tomography angiography.

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