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.

Quality assessment of radiomics models in carotid plaque: a systematic review

“…Through radiomics or texture analysis, 10 studies tended to classify unstable or risk plaques ( 25 , 27 , 29 , 32 , 33 , 39 - 43 ). Moreover, 5 studies focused on predicting unfavorable cerebral and cardiovascular events ( 28 , 35 - 38 ), 2 on evaluating stenosis or in-stent restenosis ( 26 , 30 ), 1 on assessing the robustness of radiomics features ( 34 ), and 1 on evaluating the relationship between glycated hemoglobin and ultrasound plaque textures ( 31 ). The details are shown in Table 2 .…”

“…All studies exhibited a low events per variable (EPV) value due to the large amount of candidate features and the limited carotid plaque samples. Regarding missing data, 12 studies conducted complete-case analysis and manually eliminated individuals without satisfactory images or clinical data or those who were lost to follow-up ( 25 , 26 , 28 - 30 , 32 , 36 - 41 ). In contrast, 7 studies did not describe how missing data were handled or the analytical procedures used to assess missing data ( 27 , 31 , 33 - 35 , 42 , 43 ), 4 studies lacked validation ( 28 , 30 , 31 , 39 ), and 2 studies had a high risk of bias in domain 1 for the stated inappropriate inclusion or exclusion criteria ( 33 , 39 ).…”

“…Regarding missing data, 12 studies conducted complete-case analysis and manually eliminated individuals without satisfactory images or clinical data or those who were lost to follow-up ( 25 , 26 , 28 - 30 , 32 , 36 - 41 ). In contrast, 7 studies did not describe how missing data were handled or the analytical procedures used to assess missing data ( 27 , 31 , 33 - 35 , 42 , 43 ), 4 studies lacked validation ( 28 , 30 , 31 , 39 ), and 2 studies had a high risk of bias in domain 1 for the stated inappropriate inclusion or exclusion criteria ( 33 , 39 ). The time interval between predictor assessment and outcome determination was unclear in 6 studies ( 25 , 30 , 31 , 35 , 40 , 42 ).…”

Intraplaque neovascularisation is associated with ischaemic events after carotid artery stenting: an observational prospective study

“…One study suggested that the ultrasound characteristics of a vulnerable carotid plaque are strongly associated with complications after CAS. 8 Some researchers have pointed out that carotid hypoechoic plaque has predictive value for in-stent restenosis. 9 In addition, another study with a small sample size showed that plaques detected by contrast-enhanced ultrasound (CEUS) were significantly correlated with in-stent intimal hyperplasia.…”

Texture Features of Computed Tomography Image under the Artificial Intelligence Algorithm and Its Predictive Value for Colorectal Liver Metastasis

“…It obtains relevant parameters by analyzing images and has positive significance in the judgment and analysis of computed tomography (CT) images [ 14 , 15 ]. Texture analysis quantifies tissue heterogeneity by evaluating the texture roughness and distribution within the lesion, and it can evaluate the characteristics of the lesion in more detail, which is helpful for judging the characteristics of the tumor and evaluating the prognosis and metastasis [ 16 , 17 ]. Analyzing whether texture parameters correlate with disease metastases requires modeling.…”