Dataset Information

Computed Tomography Texture Analysis of Carotid Plaque as Predictor of Unfavorable Outcome after Carotid Artery Stenting: A Preliminary Study.

ABSTRACT: Novel biomarkers are advocated to manage carotid plaques. Therefore, we aimed to test the association between textural features of carotid plaque at computed tomography angiography (CTA) and unfavorable outcome after carotid artery stenting (CAS). Between January 2010 and January 2021, were selected 172 patients (median age, 77 years; 112/172, 65% men) who underwent CAS with CTA of the supra-aortic vessels performed within prior 6 months. Standard descriptors of the density histogram were derived by open-source software automated analysis obtained by CTA plaque segmentation. Multiple logistic regression analysis, receiver operating characteristic (ROC) curve analysis and the area under the ROC (AUC) were used to identify potential prognostic variables and to assess the model performance for predicting unfavorable outcome (periprocedural death or myocardial infarction and any ipsilateral acute neurological event). Unfavorable outcome occurred in 17/172 (10%) patients (median age, 79 years; 12/17, 70% men). Kurtosis was an independent predictor of unfavorable outcome (odds ratio, 0.79; confidence interval, 0.65-0.97; p = 0.029). The predictive model for unfavorable outcome including CTA textural features outperformed the model without textural features (AUC 0.789 vs. 0.695, p = 0.004). In patients with stenotic carotid plaque, kurtosis derived by CTA density histogram analysis is an independent predictor of unfavorable outcome after CAS.

SUBMITTER: Colombi D

PROVIDER: S-EPMC8699962 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

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:BackgroundIn-stent restenosis is a crucial problem after carotid artery stenting, but the exact predictors of in-stent restenosis remain unclear. We aimed to evaluate the effect of cerebral collateral circulation on in-stent restenosis after carotid artery stenting and to establish a clinical prediction model for in-stent restenosis.MethodsThis retrospective case-control study enrolled 296 patients with severe carotid artery stenosis of C1 segment (≥70%) who underwent stent therapy from June 2015 to December 2018. Based on follow-up data, the patients were divided into the in-stent restenosis and no in-stent restenosis groups. The collateral circulation of the brain was graded according to the criteria of the American Society for Interventional and Therapy Neuroradiology/Society for Interventional Radiology (ASITN/SIR). Clinical data were collected, such as age, sex, traditional vascular risk factors, blood cell count, high-sensitivity C-reactive protein, uric acid, stenosis degree before stenting and residual stenosis rate, and medication after stenting. Binary logistic regression analysis was performed to identify potential predictors of in-stent restenosis, and a clinical prediction model for in-stent restenosis after carotid artery stenting was established.ResultsBinary logistic regression analysis showed that poor collateral circulation was an independent predictor of in-stent restenosis (P=0.003). We also found that a 1% increase in residual stenosis rate was associated with a 9% increase in the risk of in-stent restenosis (P=0.02). Ischemic stroke history (P=0.03), family history of ischemic stroke (P<0.001), in-stent restenosis history (P<0.001), and nonstandard medication after stenting (P=0.04) were predictors of in-stent restenosis. The risk of in-stent restenosis was lowest when the residual stenosis rate was 12.5% after carotid artery stenting. Furthermore, we used some significant parameters to construct a binary logistic regression prediction model for in-stent restenosis after carotid artery stenting in the form of a nomogram.ConclusionsCollateral circulation is an independent predictor of in-stent restenosis after successful carotid artery stenting, and the residual stenosis rate tends to be below 12.5% to reduce restenosis risk. The standard medication should be strictly carried out for patients after stenting to prevent in-stent restenosis.

Project description:ObjectiveTo decrease the procedural risk of carotid revascularisation it is crucial to understand the mechanisms of procedural stroke. This study analysed the features of procedural strokes associated with carotid artery stenting (CAS) and carotid endarterectomy (CEA) within the International Carotid Stenting Study (ICSS) to identify the underlying pathophysiological mechanism.Materials and methodsPatients with recently symptomatic carotid stenosis (1,713) were randomly allocated to CAS or CEA. Procedural strokes were classified by type (ischaemic or haemorrhagic), time of onset (intraprocedural or after the procedure), side (ipsilateral or contralateral), severity (disabling or non-disabling), and patency of the treated artery. Only patients in whom the allocated treatment was initiated were included. The most likely pathophysiological mechanism was determined using the following classification system: (1) carotid-embolic, (2) haemodynamic, (3) thrombosis or occlusion of the revascularised carotid artery, (4) hyperperfusion, (5) cardio-embolic, (6) multiple, and (7) undetermined.ResultsProcedural stroke occurred within 30 days of revascularisation in 85 patients (CAS 58 out of 791 and CEA 27 out of 819). Strokes were predominately ischaemic (77; 56 CAS and 21 CEA), after the procedure (57; 37 CAS and 20 CEA), ipsilateral to the treated artery (77; 52 CAS and 25 CEA), and non-disabling (47; 36 CAS and 11 CEA). Mechanisms of stroke were carotid-embolic (14; 10 CAS and 4 CEA), haemodynamic (20; 15 CAS and 5 CEA), thrombosis or occlusion of the carotid artery (15; 11 CAS and 4 CEA), hyperperfusion (9; 3 CAS and 6 CEA), cardio-embolic (5; 2 CAS and 3 CEA) and multiple causes (3; 3 CAS). In 19 patients (14 CAS and 5 CEA) the cause of stroke remained undetermined.ConclusionAlthough the mechanism of procedural stroke in both CAS and CEA is diverse, haemodynamic disturbance is an important mechanism. Careful attention to blood pressure control could lower the incidence of procedural stroke.

Dataset Information

Computed Tomography Texture Analysis of Carotid Plaque as Predictor of Unfavorable Outcome after Carotid Artery Stenting: A Preliminary Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets