Dataset Information

Computed tomography angiography vs 3 T black-blood cardiovascular magnetic resonance for identification of symptomatic carotid plaques.

ABSTRACT: The purpose of this prospective study was to perform a head-to-head comparison of the two methods most frequently used for evaluation of carotid plaque characteristics: Multi-detector Computed Tomography Angiography (MDCTA) and black-blood 3 T-cardiovascular magnetic resonance (bb-CMR) with respect to their ability to identify symptomatic carotid plaques.22 stroke unit patients with unilateral symptomatic carotid disease and >50% stenosis by duplex ultrasound underwent MDCTA and bb-CMR (TOF, pre- and post-contrast fsT1w-, and fsT2w- sequences) within 15 days of symptom onset. Both symptomatic and contralateral asymptomatic sides were evaluated. By bb-CMR, plaque morphology, composition and prevalence of complicated AHA type VI lesions (AHA-LT6) were evaluated. By MDCTA, plaque type (non-calcified, mixed, calcified), plaque density in HU and presence of ulceration and/or thrombus were evaluated. Sensitivity (SE), specificity (SP), positive and negative predictive value (PPV, NPV) were calculated using a 2-by-2-table.To distinguish between symptomatic and asymptomatic plaques AHA-LT6 was the best CMR variable and presence / absence of plaque ulceration was the best CT variable, resulting in a SE, SP, PPV and NPV of 80%, 80%, 80% and 80% for AHA-LT6 as assessed by bb-CMR and 40%, 95%, 89% and 61% for plaque ulceration as assessed by MDCTA. The combined SE, SP, PPV and NPV of bb-CMR and MDCTA was 85%, 75%, 77% and 83%, respectively.Bb-CMR is superior to MDCTA at identifying symptomatic carotid plaques, while MDCTA offers high specificity at the cost of low sensitivity. Results were only slightly improved over bb-CMR alone when combining both techniques.

SUBMITTER: Grimm JM

PROVIDER: S-EPMC4189681 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Computed tomography angiography vs 3 T black-blood cardiovascular magnetic resonance for identification of symptomatic carotid plaques.

Grimm Jochen M JM Schindler Andreas A Schwarz Florian F Cyran Clemens C CC Bayer-Karpinska Anna A Freilinger Tobias T Yuan Chun C Linn Jennifer J Trelles Miguel M Reiser Maximilian F MF Nikolaou Konstantin K Saam Tobias T

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 20141007

<h4>Background</h4>The purpose of this prospective study was to perform a head-to-head comparison of the two methods most frequently used for evaluation of carotid plaque characteristics: Multi-detector Computed Tomography Angiography (MDCTA) and black-blood 3 T-cardiovascular magnetic resonance (bb-CMR) with respect to their ability to identify symptomatic carotid plaques.<h4>Methods</h4>22 stroke unit patients with unilateral symptomatic carotid disease and >50% stenosis by duplex ultrasound u ...[more]

PMID: 25315518

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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.

Project description:ObjectivesTo determine the diagnostic accuracy of duplex ultrasonography, magnetic resonance angiography, and computed tomography angiography, alone or in combination, for the assessment of lower limb peripheral arterial disease; to evaluate the impact of these assessment methods on management of patients and outcomes; and to evaluate the evidence regarding attitudes of patients to these technologies and summarise available data on adverse events.DesignSystematic review.MethodsSearches of 11 electronic databases (to April 2005), six journals, and reference lists of included papers for relevant studies. Two reviewers independently selected studies, extracted data, and assessed quality. Diagnostic accuracy studies were assessed for quality with the QUADAS checklist.Results107 studies met the inclusion criteria; 58 studies provided data on diagnostic accuracy, one on outcomes in patients, four on attitudes of patients, and 44 on adverse events. Quality assessment highlighted limitations in the methods and quality of reporting. Most of the included studies reported results by arterial segment, rather than by limb or by patient, which does not account for the clustering of segments within patients, so specificities may be overstated. For the detection of stenosis of 50% or more in a lower limb vessel, contrast enhanced magnetic resonance angiography had the highest diagnostic accuracy with a median sensitivity of 95% (range 92-99.5%) and median specificity of 97% (64-99%). The results were 91% (89-99%) and 91% (83-97%) for computed tomography angiography and 88% (80-98%) and 96% (89-99%) for duplex ultrasonography. A controlled trial reported no significant differences in outcomes in patients after treatment plans based on duplex ultrasonography alone or conventional contrast angiography alone, though in 22% of patients supplementary contrast angiography was needed to form a treatment plan. The limited evidence available suggested that patients preferred magnetic resonance angiography (with or without contrast) to contrast angiography, with half expressing no preference between magnetic resonance angiography or duplex ultrasonography (among patients with no contraindications for magnetic resonance angiography, such as claustrophobia). Where data on adverse events were available, magnetic resonance angiography was associated with the highest proportion of adverse events, but these were mild. The most severe adverse events, although rare, were mainly associated with contrast angiography.ConclusionsContrast enhanced magnetic resonance angiography seems to be more specific than computed tomography angiography (that is, better at ruling out stenosis over 50%) and more sensitive than duplex ultrasonography (that is, better at ruling in stenosis over 50%) and was generally preferred by patients over contrast angiography. Computed tomography angiography was also preferred by patients over contrast angiography; no data on patients' preference between duplex ultrasonography and contrast angiography were available. Where available, contrast enhanced magnetic resonance angiography might be a viable alternative to contrast angiography.

Project description:ObjectivesThe aim of this study was to precisely phenotype culprit and nonculprit lesions in myocardial infarction (MI) and lesions in stable coronary artery disease (CAD) using coronary computed tomography angiography (CTA)-based radiomic analysis.BackgroundIt remains debated whether any single coronary atherosclerotic plaque within the vulnerable patient exhibits unique morphology conferring an increased risk of clinical events.MethodsA total of 60 patients with acute MI prospectively underwent coronary CTA before invasive angiography and were matched to 60 patients with stable CAD. For all coronary lesions, high-risk plaque (HRP) characteristics were qualitatively assessed, followed by semiautomated plaque quantification and extraction of 1,103 radiomic features. Machine learning models were built to examine the additive value of radiomic features for discriminating culprit lesions over and above HRP and plaque volumes.ResultsCulprit lesions had higher mean volumes of noncalcified plaque (NCP) and low-density noncalcified plaque (LDNCP) compared with the highest-grade stenosis nonculprits and highest-grade stenosis stable CAD lesions (NCP: 138.1 mm3 vs 110.7 mm3 vs 102.7 mm3; LDNCP: 14.2 mm3 vs 9.8 mm3 vs 8.4 mm3; both Ptrend < 0.01). In multivariable linear regression adjusted for NCP and LDNCP volumes, 14.9% (164 of 1,103) of radiomic features were associated with culprits and 9.7% (107 of 1,103) were associated with the highest-grade stenosis nonculprits (critical P < 0.0007) when compared with highest-grade stenosis stable CAD lesions as reference. Hierarchical clustering of significant radiomic features identified 9 unique data clusters (latent phenotypes): 5 contained radiomic features specific to culprits, 1 contained features specific to highest-grade stenosis nonculprits, and 3 contained features associated with either lesion type. Radiomic features provided incremental value for discriminating culprit lesions when added to a machine learning model containing HRP and plaque volumes (area under the receiver-operating characteristic curve 0.86 vs 0.76; P = 0.004).ConclusionsCulprit lesions and highest-grade stenosis nonculprit lesions in MI have distinct radiomic signatures compared with lesions in stable CAD. Within the vulnerable patient may exist individual vulnerable plaques identifiable by coronary CTA-based precision phenotyping.

Project description:Pericoronary adipose tissue (PCAT) features on Computed Tomography (CT) have been shown to reflect local inflammation and increased cardiovascular risk. Our goal was to determine whether PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified vulnerable-plaque characteristics (e.g., microchannels (MC) and thin-cap fibroatheroma (TCFA)). The CCTA and IVOCT images of 30 lesions from 25 patients were registered. The vessels with vulnerable plaques were identified from the registered IVOCT images. The PCAT-radiomics features were extracted from the CCTA images for the lesion region of interest (PCAT-LOI) and the entire vessel (PCAT-Vessel). We extracted 1356 radiomic features, including intensity (first-order), shape, and texture features. The features were reduced using standard approaches (e.g., high feature correlation). Using stratified three-fold cross-validation with 1000 repeats, we determined the ability of PCAT-radiomics features from CCTA to predict IVOCT vulnerable-plaque characteristics. In the identification of TCFA lesions, the PCAT-LOI and PCAT-Vessel radiomics models performed comparably (Area Under the Curve (AUC) ± standard deviation 0.78 ± 0.13, 0.77 ± 0.14). For the identification of MC lesions, the PCAT-Vessel radiomics model (0.89 ± 0.09) was moderately better associated than the PCAT-LOI model (0.83 ± 0.12). In addition, both the PCAT-LOI and the PCAT-Vessel radiomics model identified coronary vessels thought to be highly vulnerable to a similar standard (i.e., both TCFA and MC; 0.88 ± 0.10, 0.91 ± 0.09). The most favorable radiomic features tended to be those describing the texture and size of the PCAT. The application of PCAT radiomics can identify coronary vessels with TCFA or MC, consistent with IVOCT. Furthermore, the use of CCTA radiomics may improve risk stratification by noninvasively detecting vulnerable-plaque characteristics that are only visible with IVOCT.

Project description:ImportanceAdvanced imaging for patient selection in mechanical thrombectomy is not widely available.ObjectiveTo compare the clinical outcomes of patients selected for mechanical thrombectomy by noncontrast computed tomography (CT) vs those selected by computed tomography perfusion (CTP) or magnetic resonance imaging (MRI) in the extended time window.Design, setting, and participantsThis multinational cohort study included consecutive patients with proximal anterior circulation occlusion stroke presenting within 6 to 24 hours of time last seen well from January 2014 to December 2020. This study was conducted at 15 sites across 5 countries in Europe and North America. The duration of follow-up was 90 days from stroke onset.ExposuresComputed tomography with Alberta Stroke Program Early CT Score, CTP, or MRI.Main outcomes and measuresThe primary end point was the distribution of modified Rankin Scale (mRS) scores at 90 days (ordinal shift). Secondary outcomes included the rates of 90-day functional independence (mRS scores of 0-2), symptomatic intracranial hemorrhage, and 90-day mortality.ResultsOf 2304 patients screened for eligibility, 1604 patients were included, with a median (IQR) age of 70 (59-80) years; 848 (52.9%) were women. A total of 534 patients were selected to undergo mechanical thrombectomy by CT, 752 by CTP, and 318 by MRI. After adjustment of confounders, there was no difference in 90-day ordinal mRS shift between patients selected by CT vs CTP (adjusted odds ratio [aOR], 0.95 [95% CI, 0.77-1.17]; P = .64) or CT vs MRI (aOR, 0.95 [95% CI, 0.8-1.13]; P = .55). The rates of 90-day functional independence (mRS scores 0-2 vs 3-6) were similar between patients selected by CT vs CTP (aOR, 0.90 [95% CI, 0.7-1.16]; P = .42) but lower in patients selected by MRI than CT (aOR, 0.79 [95% CI, 0.64-0.98]; P = .03). Successful reperfusion was more common in the CT and CTP groups compared with the MRI group (474 [88.9%] and 670 [89.5%] vs 250 [78.9%]; P < .001). No significant differences in symptomatic intracranial hemorrhage (CT, 42 [8.1%]; CTP, 43 [5.8%]; MRI, 15 [4.7%]; P = .11) or 90-day mortality (CT, 125 [23.4%]; CTP, 159 [21.1%]; MRI, 62 [19.5%]; P = .38) were observed.Conclusions and relevanceIn patients undergoing proximal anterior circulation mechanical thrombectomy in the extended time window, there were no significant differences in the clinical outcomes of patients selected with noncontrast CT compared with those selected with CTP or MRI. These findings have the potential to widen the indication for treating patients in the extended window using a simpler and more widespread noncontrast CT-only paradigm.

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.

Project description:ObjectivesTo compare the detection of relevant extracardiac findings (ECFs) on coronary computed tomography angiography (CTA) and invasive coronary angiography (ICA) and evaluate the potential clinical benefit of their detection.MethodsThis is the prespecified subanalysis of ECFs in patients presenting with a clinical indication for ICA based on atypical angina and suspected coronary artery disease (CAD) included in the prospective single-center randomized controlled Coronary Artery Disease Management (CAD-Man) study. ECFs requiring immediate therapy and/or further workup including additional imaging were defined as clinically relevant. We evaluated the scope of ECFs in 329 patients and analyzed the potential clinical benefit of their detection.ResultsECFs were detected in 107 of 329 patients (32.5%; CTA: 101/167, 60.5%; ICA: 6/162, 3.7%; p < .001). Fifty-nine patients had clinically relevant ECFs (17.9%; CTA: 55/167, 32.9%; ICA: 4/162, 2.5%; p < .001). In the CTA group, ECFs potentially explained atypical chest pain in 13 of 101 patients with ECFs (12.9%). After initiation of therapy, chest pain improved in 4 (4.0%) and resolved in 7 patients (6.9%). Follow-up imaging was recommended in 33 (10.0%; CTA: 30/167, 18.0%; ICA: 3/162, 1.9%) and additional clinic consultation in 26 patients (7.9%; CTA: 25/167, 15.0%; ICA: 1/162, 0.6%). Malignancy was newly diagnosed in one patient (0.3%; CTA: 1/167, 0.6%; ICA: 0).ConclusionsIn this randomized study, CTA but not ICA detected clinically relevant ECFs that may point to possible other causes of chest pain in patients without CAD. Thus, CTA might preclude the need for ICA in those patients.Trial registrationNCT Unique ID: 00844220 KEY POINTS: • CTA detects ten times more clinically relevant ECFs than ICA. • Actionable clinically relevant ECFs affect patient management and therapy and may thus improve chest pain. • Detection of ECFs explaining chest pain on CTA might preclude the need for performing ICA.

Dataset Information

Computed tomography angiography vs 3 T black-blood cardiovascular magnetic resonance for identification of symptomatic carotid plaques.

Publications

Computed tomography angiography vs 3 T black-blood cardiovascular magnetic resonance for identification of symptomatic carotid plaques.

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