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

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods

Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case

Computer aided diagnosis of coronary artery disease, myocardial infarction and carotid atherosclerosis using ultrasound images: a review

The diagnosis of Coronary Artery Disease (CAD), Myocardial Infarction (MI) and carotid atherosclerosis is of paramount importance, as these cardiovascular diseases may cause medical complications and large number of death. Ultrasound (US) is a widely used imaging modality, as it captures moving images and image features correlate well with results obtained from other imaging methods. Furthermore, US does not use ionizing radiation and it is economical when compared to other imaging modalities. However, reading US images takes time and the relationship between image and tissue composition is complex. Therefore, the diagnostic accuracy depends on both time taken to read the images and experience of the screening practitioner. Computer support tools can reduce the inter-operator variability with lower subject specific expertise, when appropriate processing methods are used. In the current review, we analysed automatic detection methods for the diagnosis of CAD, MI and carotid atherosclerosis based on thoracic and Intravascular Ultrasound (IVUS). We found that IVUS is more often used than thoracic US for CAD. But for MI and carotid atherosclerosis IVUS is still in the experimental stage. Furthermore, thoracic US is more often used than IVUS for computer aided diagnosis systems

Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events

open20siAcknowledgements: EPVL is undertaking a PhD funded by the Cambridge School of Clinical Medicine, Frank Edward Elmore Fund and the Medical Research Council’s Doctoral Training Partnership [award reference: 1966157]. JMT is supported by a Wellcome Trust Clinical Research Career Development Fellowship [211100/Z/18/Z], the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre and the British Heart Foundation Cambridge Centre of Research Excellence. NRE was supported by a Research Training Fellowship from The Dunhill Medical Trust [RTF44/0114]. MMC was supported by fellowships from the Royal College of Surgeons of England, and the British Heart Foundation [BHF; FS/16/29/31957]. HP is undertaking a PhD with a BHF CRE studentship. FJG is an NIHR Senior Investigator. LR and ES were supported by The Mark Foundation for Cancer Research and Cancer Research UK (CRUK) Cambridge Centre [C9685/A25177]. MR is supported by AstraZeneca Oncology R&D. ES receives additional support provided by the NIHR Cambridge Biomedical Research Centre. FAG receives funding from CRUK. EAW receives support from the NIHR CRN. CBS acknowledges support from the Leverhulme Trust project on ‘Breaking the non-convexity barrier’, the Philip Leverhulme Prize, the EPSRC grants EP/S026045/1 and EP/T003553/1, the EPSRC Centre Nr. EP/N014588/1, the Wellcome Innovator Award RG98755, European Union Horizon 2020 research and innovation programmes under the Marie Skodowska-Curie grant agreement No. 777826 NoMADS and No. 691070 CHiPS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. JHFR is part-supported by the NIHR Cambridge Biomedical Research Centre, the British Heart Foundation, HEFCE, the Wellcome Trust and the EPSRC grant [EP/N014588/1] for the University of Cambridge Centre for Mathematical Imaging in Healthcare. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk.noneLe E.P.V.; Rundo L.; Tarkin J.M.; Evans N.R.; Chowdhury M.M.; Coughlin P.A.; Pavey H.; Wall C.; Zaccagna F.; Gallagher F.A.; Huang Y.; Sriranjan R.; Le A.; Weir-McCall J.R.; Roberts M.; Gilbert F.J.; Warburton E.A.; Schonlieb C.-B.; Sala E.; Rudd J.H.F.Le E.P.V.; Rundo L.; Tarkin J.M.; Evans N.R.; Chowdhury M.M.; Coughlin P.A.; Pavey H.; Wall C.; Zaccagna F.; Gallagher F.A.; Huang Y.; Sriranjan R.; Le A.; Weir-McCall J.R.; Roberts M.; Gilbert F.J.; Warburton E.A.; Schonlieb C.-B.; Sala E.; Rudd J.H.F

Atherosclerotic Plaque Tissue Characterization: An OCT-Based Machine Learning Algorithm With ex vivo Validation

There is a need to develop a validated algorithm for plaque characterization which can help to facilitate the standardization of optical coherence tomography (OCT) image interpretation of plaque morphology, and improve the efficiency and accuracy in the application of OCT imaging for the quantitative assessment of plaque vulnerability. In this study, a machine learning algorithm was implemented for characterization of atherosclerotic plaque components by intravascular OCT using ex vivo carotid plaque tissue samples. A total of 31 patients underwent carotid endarterectomy and the ex vivo carotid plaques were imaged with OCT. Optical parameter, texture features and relative position of pixels were extracted within the region of interest and then used to quantify the tissue characterization of plaque components. The potential of individual and combined feature set to discriminate tissue components was quantified using sensitivity, specificity, accuracy. The results show there was a lower classification accuracy in the calcified tissue than the fibrous tissue and lipid tissue. The pixel-wise classification accuracy obtained by the developed method, to characterize the fibrous, calcified and lipid tissue by comparing with histology, were 80.0, 62.0, and 83.1, respectively. The developed algorithm was capable of characterizing plaque components with an excellent accuracy using the combined feature set

Novel ultrasound features for the identification of the vulnerable carotid plaque

Background: The identification of the vulnerable carotid plaque is of paramount importance in order to prevent the significant stroke-related mortality and morbidity. Currently the clinical decision-making around this condition is based on the traditional ultrasound evaluation of the degree of stenosis. However, there is emerging evidence supporting that this is not sufficient for all patients. Aim of this thesis: The evaluation of novel carotid plaque features for the characterisation of plaque composition, volume and motion using 2 and 3 dimensional ultrasound technology. The ultimate goal is to identify novel sensitive imaging markers for carotid plaque characterisation and stroke-risk stratification. Methods: The Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) Study was a large prospective multicentre trial that was recently completed. A post-hoc analysis of the sonographic and clinical data from this study was performed in order to evaluate the effectiveness of novel ultrasound texture features, such as second order statics, on stroke-risk prediction. In addition, the change of specific texture features and degree of stenosis during the ACSRS follow-up time (8 years) and their importance for stroke prediction was evaluated. In order to assess the potential of 3D ultrasound carotid imaging we also developed a special methodology using a 3D broadband, linear array probe and the Q-lab software. This methodology was then applied in a clinical, cross-sectional study of patients with symptomatic and asymptomatic carotid disease. Finally we developed a carotid plaque motion analysis methodology that we tested on a feasibility study. Results: The post-hoc analysis of more than 1, 000 patients from the ACSRS database showed that there are novel ultrasound features of plaque homogeneity that can contribute to plaque characterisation and improve stroke-risk prediction. Similarly our results suggest that the change of degree of stenosis or plaque’s composition through time might have significant predictive value when combined with the above novel features. The study in 3D ultrasound prospectively assessed more than 80 people with symptomatic and asymptomatic carotid disease with both 2 and 3D carotid ultrasound without, though, revealing any significant benefit from the use of 3D imaging in terms of stroke-risk prediction. Finally, our feasibility study on plaque motion analysis showed that it is possible to objectively characterise plaque motion, using ultrasound and dedicated software without complicated reconstructions. Conclusion: The use of novel 2D ultrasound texture features in combination with traditional ones can improve the stroke-risk stratification. 3D ultrasound is a promising new approach, however, the current technology does not appear to offer a significant benefit in comparison to cheaper traditional 2D ultrasound for carotid plaque evaluation. Further research is warranted on this issue.Open Acces

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland