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

Patient-specific modeling of the biomechanics of vulnerable coronary artery plaques

Coronary artery atherosclerosis is a local, multifactorial, complex disease, and the leading cause of death in the US. Complex interactions between biochemical transport and biomechanical forces influence disease growth. Wall shear stress (WSS) affects coronary artery atherosclerosis by inducing endothelial cell mechanotransduction and by controlling the near- wall transport processes involved in atherosclerosis. The current management guidelines for detection of atherosclerotic plaques focus on morphological characterizations and the blockage percentage of the stenosis based on coronary computed tomography angiography (CCTA). Despite the progress achieved in therapeutics, the relation between hemodynamic environment and the composition of atherosclerotic plaques remains unexplored. This dissertation is divided into two main sections: the association between hemodynamics/biotransport and longitudinal changes in the plaque vulnerability characteristics and developing a 1D automatic vascular network generation package with the ability to be coupled with a 3D patient-specific model. Biochemical-specific mass transport models were developed to study low-density lipoprotein, nitric oxide, adenosine triphosphate, oxygen, monocyte chemoattractant protein-1, and monocyte transport. The transport results were compared with WSS vectors and WSS Lagrangian coherent structures (WSS LCS). High WSS magnitude protected against atherosclerosis by increasing the production or flux of atheroprotective biochemicals and decreasing the near-wall localization of atherogenic biochemicals. Low WSS magnitude promoted atherosclerosis by increasing atherogenic biochemical localization. To find the association between hemodynamics/biotransport and longitudinal changes in the atherosclerotic plaque characteristics, a plaque quantification software was developed with the aim of performing a segment-specific assessment to accurately calculate the volumes of low attenuation plaque (LAP), fibrous plaque (FP), calcium plaque (CP), and vessel wall and identify the quantitative plaque characteristics including spotty calcification, presence of napkin-ring sign, and positive remodeling. The changes in the different plaque characteristics were compared against the hemodynamic/biotransport parameters. The results showed that WSS magnitude is moderately correlated with the longitudinal changes in LAP, FP, and vessel wall volumes. Also, WSS magnitude and local concentration of nitric oxide (NO) showed a meaningful correlation with the presence of positive remodeling in the follow-up. A hybrid 1D-3D solver was developed in Simvascular software and validated against the existing data in the literature. The results of our coupled 1D-3D solver showed a good agreement with the 3D, deformable wall models. This solver can be used to solve the blood flow in a large network of 1D vessels coupled with a patient-specific 3D model. Finally, an automatic vascular network generation framework was developed using the Constraint Constructive Optimization (CCO) algorithm to study the generation of arterial trees based on theoretical perfusion maps. The algorithm simulated angiogenesis by optimizing the total vessel volume governed by physiological and geometrical constraints

Machine Learning for Biomedical Application

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images