Texture Analysis Based on Vascular Ultrasound to Identify the Vulnerable Carotid Plaques

Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus

Background and objective

Type 2 Diabetes Mellitus (T2DM) with insulin resistance (IR) is prone to damage the vascular endothelial, leading to the formation of vulnerable carotid plaques and increasing ischemic stroke (IS) risk. The purpose of this study is to develop a nomogram model based on carotid ultrasound radiomics for predicting IS risk in T2DM patients.

Methods

198 T2DM patients were enrolled and separated into study and control groups based on IS history. After manually delineating carotid plaque region of interest (ROI) from images, radiomics features were identified and selected using the least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (RS). A combinatorial logistic machine learning model and nomograms were created using RS and clinical features like the triglyceride-glucose index. The three models were assessed using area under curve (AUC) and decision curve analysis (DCA).

Results

Patients were divided into the training set and the testing set by the ratio of 0.7. 4 radiomics features were selected. RS and clinical variables were all statically significant in the training set and were used to create a combination model and a prediction nomogram. The combination model (radiomics + clinical nomogram) had the largest AUC in both the training set and the testing set (0.898 and 0.857), and DCA analysis showed that it had a higher overall net benefit compared to the other models.

Conclusions

This study created a carotid ultrasound radiomics machine-learning-based IS risk nomogram for T2DM patients with carotid plaques. Its diagnostic performance and clinical prediction capabilities enable accurate, convenient, and customized medical care.

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

Background

Although imaging techniques provide information about the morphology and stability of carotid plaque, they are operator dependent and may miss certain subtleties. A variety of radiomics models for carotid plaque have recently been proposed for identifying vulnerable plaques and predicting cardiovascular and cerebrovascular diseases. The purpose of this review was to assess the risk of bias, reporting, and methodological quality of radiomics models for carotid atherosclerosis plaques.

Methods

A systematic search was carried out to identify available literature published in PubMed, Web of Science, and the Cochrane Library up to March 2023. Studies that developed and/or validated machine learning models based on radiomics data to identify and/or predict unfavorable cerebral and cardiovascular events in carotid plaque were included. The basic information of each piece of included literature was identified, and the reporting quality, risk of bias, and radiomics methodology quality were assessed according the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) checklist, the Prediction Model Risk of Bias Assessment Tool (PROBAST), and the radiomics quality score (RQS), respectively.

Results

A total of 2,738 patients from 19 studies were included. The mean overall TRIPOD adherence rate was 66.1% (standard deviation 12.8%), with a range of 45-87%. All studies had a high overall risk of bias, with the analysis domain being the most common source of bias. The mean RQS was 9.89 (standard deviation 5.70), accounting for 27.4% of the possible maximum value of 36. The mean area under the curve for diagnostic or predictive properties of these included radiomics models was 0.876±0.09, with a range of 0.741-0.989.

Conclusions

Radiomics models may have value in the assessment of carotid plaque, the overall scientific validity and reporting quality of current carotid plaque radiomics reports are still lacking, and many barriers must be overcome before these models can be applied in clinical practice.

Optoacoustic biomarkers of lipids, hemorrhage and inflammation in carotid atherosclerosis

Imaging plays a critical role in exploring the pathophysiology and enabling the diagnostics and therapy assessment in carotid artery disease. Ultrasonography, computed tomography, magnetic resonance imaging and nuclear medicine techniques have been used to extract of known characteristics of plaque vulnerability, such as inflammation, intraplaque hemorrhage and high lipid content. Despite the plethora of available techniques, there is still a need for new modalities to better characterize the plaque and provide novel biomarkers that might help to detect the vulnerable plaque early enough and before a stroke occurs. Optoacoustics, by providing a multiscale characterization of the morphology and pathophysiology of the plaque could offer such an option. By visualizing endogenous (e.g., hemoglobin, lipids) and exogenous (e.g., injected dyes) chromophores, optoacoustic technologies have shown great capability in imaging lipids, hemoglobin and inflammation in different applications and settings. Herein, we provide an overview of the main optoacoustic systems and scales of detail that enable imaging of carotid plaques in vitro, in small animals and humans. Finally, we discuss the limitations of this novel set of techniques while investigating their potential to enable a deeper understanding of carotid plaque pathophysiology and possibly improve the diagnostics in future patients with carotid artery disease.

The Complementary Effects of Dabigatran Etexilate and Exercise Training on the Development and Stability of the Atherosclerotic Lesions in Diabetic ApoE Knockout Mice

Aim: To determine the complementary effects of dabigatran etexilate (DE), exercise training (ET), and combination (DE + ET) on the development and stability of the atherosclerotic lesions in diabetic apoE knockout (apoE-/-) mice. Methods: In 48 male apoE-/- diabetic mice, streptozotocin (STZ) was induced for 5 consecutive days. Mice received a high-fat diet (HFD) for 8 weeks and then were randomized into four groups (1. Control/CG, 2. DEG: HFD with DE, 3. ETG: ET on treadmill, 4. DE + ETG: combination DE and ET treatment). At the end of the eighth week, all mice were euthanatized and morphometry of the aortic lesions at the level of aortic valve was obtained. Collagen, elastin, MCP-1, TNF-a, matrix metalloproteinases (MMP-2,-3,-9), and TIMP-1 concentrations within plaques at the aortic valve were determined. Results: All active groups had significantly smaller aorta stenosis (DEG:7.9 ± 2.2%, ETG:17.3 ± 5.3%, DE + ETG:7.1 ± 2.7%) compared to CG (23.3 ± 5.5% p < 0.05), reduced the relative intra-plaque content of MCP-1, macrophages, MMP-3, and MMP-9, and considerably increased collagen, elastin, and TIMP-1 (p < 0.05). Group 4 showed the most pronounced results (p < 0.05). Both DEG and DE + ETG significantly reduced MMP-2 and TNF-a concentrations compared to ETG and CG (p < 0.010). Conclusion: DE and ET treatment of diabetic apoE-/- mice resulted in complementary amelioration of atherosclerotic lesions development and stability, mediated by the anti-inflammatory modulation of both DE and ET.

Applications of artificial intelligence-based models in vulnerable carotid plaque

Carotid atherosclerotic disease is a widely acknowledged risk factor for ischemic stroke, making it a major concern on a global scale. To alleviate the socio-economic impact of carotid atherosclerotic disease, crucial objectives include prioritizing prevention efforts and early detection. So far, the degree of carotid stenosis has been regarded as the primary parameter for risk assessment and determining appropriate therapeutic interventions. Histopathological and imaging-based studies demonstrated important differences in the risk of cardiovascular events given a similar degree of luminal stenosis, identifying plaque structure and composition as key determinants of either plaque vulnerability or stability. The application of Artificial Intelligence (AI)-based techniques to carotid imaging can offer several solutions for tissue characterization and classification. This review aims to present a comprehensive overview of the main concepts related to AI. Additionally, we review the existing literature on AI-based models in ultrasound (US), computed tomography (CT), and Magnetic Resonance Imaging (MRI) for vulnerable plaque detection, and we finally examine the advantages and limitations of these AI approaches.

Artificial Intelligence in Symptomatic Carotid Plaque Detection: A Narrative Review

Identifying atherosclerotic disease is the mainstay for the correct diagnosis of the large artery atherosclerosis ischemic stroke subtype and for choosing the right therapeutic strategy in acute ischemic stroke. Classification into symptomatic and asymptomatic plaque and estimation of the cardiovascular risk are essential to select patients eligible for pharmacological and/or surgical therapy in order to prevent future cerebral ischemic events. The difficulties in a “vulnerability” definition and the methodical issues concerning its detectability and quantification are still subjects of debate. Non-invasive imaging studies commonly used to detect arterial plaque are computed tomographic angiography, magnetic resonance imaging, and ultrasound. Characterization of a carotid plaque type using the abovementioned imaging modalities represents the basis for carotid atherosclerosis management. Classification into symptomatic and asymptomatic plaque and estimation of the cardiovascular risk are essential to select patients eligible for pharmacological and/or surgical therapy in order to prevent future cerebral ischemic events. In this setting, artificial intelligence (AI) can offer suggestive solutions for tissue characterization and classification concerning carotid artery plaque imaging by analyzing complex data and using automated algorithms to obtain a final output. The aim of this review is to provide overall knowledge about the role of AI models applied to non-invasive imaging studies for the detection of symptomatic and vulnerable carotid plaques.