International Union of Angiology (IUA) consensus paper on imaging strategies in atherosclerotic carotid artery imaging: From basic strategies to advanced approaches

Cardiovascular disease (CVD) is the leading cause of mortality and disability in developed countries. According to WHO, an estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to major adverse cardiac and cerebral events. Early detection and care for individuals at high risk could save lives, alleviate suffering, and diminish economic burden associated with these diseases. Carotid artery disease is not only a well-established risk factor for ischemic stroke, contributing to 10%–20% of strokes or transient ischemic attacks (TIAs), but it is also a surrogate marker of generalized atherosclerosis and a predictor of cardiovascular events. In addition to diligent history, physical examination, and laboratory detection of metabolic abnormalities leading to vascular changes, imaging of carotid arteries adds very important information in assessing stroke and overall cardiovascular risk. Spanning from carotid intima-media thickness (IMT) measurements in arteriopathy to plaque burden, morphology and biology in more advanced disease, imaging of carotid arteries could help not only in stroke prevention but also in ameliorating cardiovascular events in other territories (e.g. in the coronary arteries). While ultrasound is the most widely available and affordable imaging methods, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), their combination and other more sophisticated methods have introduced novel concepts in detection of carotid plaque characteristics and risk assessment of stroke and other cardiovascular events. However, in addition to robust progress in usage of these methods, all of them have limitations which should be taken into account. The main purpose of this consensus document is to discuss pros but also cons in clinical, epidemiological and research use of all these techniques

Insight into Carotid Atherosclerotic Plaque Development with CT Angiography

Stroke is a leading cause of mortality and morbidity. Atherosclerotic disease of the carotid arteries is an important cause of ischemic stroke. The general aim of this thesis is to contribute to the knowledge on the pathophysiology of atherosclerosis by means of imaging of the atherosclerotic carotid plaque in vivo. This thesis focusses on: - Quantification of imaging biomarkers of carotid atherosclerotic disease with CT angiography; - The investigation on the role of carotid plaque surface (i.e. ulceration) as an imaging biomarker of plaque instability; - The study of plaque development and its determinants using serial CTA imaging

Automated Quantification of Atherosclerosis in CTA of Carotid Arteries

How is the human body built and how does it function? What are the causes of disease, and where is disease located? Throughout the history of mankind these questions were answered by the use of invasive methods that included the “opening” of the human body, mainly cadavers. Thanks to these invasive techniques the first precise and complete anatomy works started to appear in the 16th century. The most influential works were published by Leonardo da Vinci and the anatomist and physician Andreas Vesalius. The discovery of X-rays in 1895, and their use for medical applications, introduced a new era, in which non-invasive imaging of the functioning human body became feasible. Nowadays, medical imaging includes many different imaging modalities, such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), nuclear and optical imaging, and has become an indispensable diagnostic tool for a wide range of applications. Initially, the application of medical imaging focused on the visualization of anatomy and on the detection and localization of disease. However, with the development of different modalities it has evolved into a much more versatile tool providing important information on e.g. physiology and organ function, biochemistry and metabolism using nuclear imaging (mainly positron emission tomography (PET) imaging), molecular and processes on the molecular and cellular level using molecular imaging techniques

Radiomics and Machine Learning in the Prediction of Cardiovascular Disease

Carotid atherosclerosis is a major risk factor for ischaemic stroke which is a leading cause of death worldwide. For stroke survivors, 1 in 4 will have another stroke within five years. Carotid CT angiography (CTA) is commonly performed following an ischaemic stroke or transient ischemic attack to help guide patient management in the secondary prevention of stroke. For example, carotid endarterectomy surgery plus medical therapy or medical therapy alone. The degree of carotid stenosis is the mainstay in making this decision and uses only one aspect of anatomical information that can be obtained from a carotid CTA scan. Radiomics, sometimes called ‘texture analysis’, is the extraction of quantitative data from medical images that may not be apparent to the naked eye and has already demonstrated clinical utility in oncology for applications ranging from lesion characterisation to tumour grading and prognostication. Machine learning refers to the process of learning from experience (in this case data), rather than following pre-programmed rules. This thesis presents the findings of a proof-of-principle study to assess the value of radiomics in identifying the ‘vulnerable plaque’ and the ‘vulnerable patient’ within the context of cerebrovascular events. To evaluate the potential of radiomic features as imaging biomarkers, their reproducibility and robustness to morphological perturbations were assessed, as well as their biological associations with both PET and immunohistochemistry data. The ability of radiomic features to classify different carotid artery types, namely, culprit, non-culprit and asymptomatic carotid arteries was assessed using several machine learning classifiers. This was subsequently compared with a deep learning approach, which has greater capacity for data mining than feature-based machine learning approaches. Overall, radiomics could extract further useful information from carotid CTA scans. Culprit versus non-culprit carotid arteries in symptomatic patients and asymptomatic carotid arteries from asymptomatic patients had different radiomic profiles that could be leveraged using machine learning for better classification performance than carotid calcification or carotid PET imaging alone. Reliable and robust CT-based carotid radiomic features were identified that were associated with the degree of inflammation underlying the carotid artery. If validated with future prospective studies, this has the potential to improve personalised patient care in stroke management and advance clinical decision-making.Cambridge School of Clinical Medicine, the Medical Research Council's Doctoral Training Partnership and the Frank Edward Elmore Fun