Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach
Purpose: To assess radiomics and deep learning (DL) methods in identifying symptomatic Carotid Artery Disease (CAD) from carotid CT angiography (CTA) images. We further compare the performance of these novel methods to the conventional calcium score. Methods: Carotid CT angiography (CTA) images from...
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Elsevier
2024-12-01
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| Series: | European Journal of Radiology Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352047724000492 |
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| author | Elizabeth P.V. Le Mark Y.Z. Wong Leonardo Rundo Jason M. Tarkin Nicholas R. Evans Jonathan R. Weir-McCall Mohammed M. Chowdhury Patrick A. Coughlin Holly Pavey Fulvio Zaccagna Chris Wall Rouchelle Sriranjan Andrej Corovic Yuan Huang Elizabeth A. Warburton Evis Sala Michael Roberts Carola-Bibiane Schönlieb James H.F. Rudd |
| author_facet | Elizabeth P.V. Le Mark Y.Z. Wong Leonardo Rundo Jason M. Tarkin Nicholas R. Evans Jonathan R. Weir-McCall Mohammed M. Chowdhury Patrick A. Coughlin Holly Pavey Fulvio Zaccagna Chris Wall Rouchelle Sriranjan Andrej Corovic Yuan Huang Elizabeth A. Warburton Evis Sala Michael Roberts Carola-Bibiane Schönlieb James H.F. Rudd |
| author_sort | Elizabeth P.V. Le |
| collection | DOAJ |
| description | Purpose: To assess radiomics and deep learning (DL) methods in identifying symptomatic Carotid Artery Disease (CAD) from carotid CT angiography (CTA) images. We further compare the performance of these novel methods to the conventional calcium score. Methods: Carotid CT angiography (CTA) images from symptomatic patients (ischaemic stroke/transient ischaemic attack within the last 3 months) and asymptomatic patients were analysed. Carotid arteries were classified into culprit, non-culprit and asymptomatic. The calcium score was assessed using the Agatston method. 93 radiomic features were extracted from regions-of-interest drawn on 14 consecutive CTA slices. For DL, convolutional neural networks (CNNs) with and without transfer learning were trained directly on CTA slices. Predictive performance was assessed over 5-fold cross validated AUC scores. SHAP and GRAD-CAM algorithms were used for explainability. Results: 132 carotid arteries were analysed (41 culprit, 41 non-culprit, and 50 asymptomatic). For asymptomatic vs symptomatic arteries, radiomics attained a mean AUC of 0.96(± 0.02), followed by DL 0.86(± 0.06) and then calcium 0.79(± 0.08). For culprit vs non-culprit arteries, radiomics achieved a mean AUC of 0.75(± 0.09), followed by DL 0.67(± 0.10) and then calcium 0.60(± 0.02). For multi-class classification, the mean AUCs were 0.95(± 0.07), 0.79(± 0.05), and 0.71(± 0.07) for radiomics, DL and calcium, respectively. Explainability revealed consistent patterns in the most important radiomic features. Conclusions: Our study highlights the potential of novel image analysis techniques in extracting quantitative information beyond calcification in the identification of CAD. Though further work is required, the transition of these novel techniques into clinical practice may eventually facilitate better stroke risk stratification. |
| format | Article |
| id | doaj-art-5f3bf2f517c440329ea8eac866760768 |
| institution | OA Journals |
| issn | 2352-0477 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Journal of Radiology Open |
| spelling | doaj-art-5f3bf2f517c440329ea8eac8667607682025-08-20T01:56:20ZengElsevierEuropean Journal of Radiology Open2352-04772024-12-011310059410.1016/j.ejro.2024.100594Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approachElizabeth P.V. Le0Mark Y.Z. Wong1Leonardo Rundo2Jason M. Tarkin3Nicholas R. Evans4Jonathan R. Weir-McCall5Mohammed M. Chowdhury6Patrick A. Coughlin7Holly Pavey8Fulvio Zaccagna9Chris Wall10Rouchelle Sriranjan11Andrej Corovic12Yuan Huang13Elizabeth A. Warburton14Evis Sala15Michael Roberts16Carola-Bibiane Schönlieb17James H.F. Rudd18Department of Medicine, University of Cambridge, United KingdomDepartment of Medicine, University of Cambridge, United Kingdom; Corresponding author.Department of Radiology, University of Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, United Kingdom; Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, ItalyDepartment of Medicine, University of Cambridge, United KingdomDepartment of Clinical Neurosciences, University of Cambridge, United KingdomDepartment of Radiology, University of Cambridge, United Kingdom; Department of Radiology, Royal Papworth Hospital, Cambridge, UKDivision of Vascular Surgery, Department of Surgery, University of Cambridge, United KingdomDepartment of Vascular Surgery, University of Leeds, United KingdomDivision of Experimental Medicine and Immunotherapeutics, University of Cambridge, United KingdomDepartment of Radiology, University of Cambridge, United Kingdom; Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom; Investigative Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United KingdomDepartment of Medicine, University of Cambridge, United KingdomDepartment of Medicine, University of Cambridge, United KingdomDepartment of Medicine, University of Cambridge, United KingdomDepartment of Medicine, University of Cambridge, United Kingdom; Department of Radiology, University of Cambridge, United Kingdom; EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, United KingdomDepartment of Clinical Neurosciences, University of Cambridge, United KingdomDipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy; Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, ItalyDepartment of Medicine, University of Cambridge, United Kingdom; EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, United Kingdom; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United KingdomEPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, United KingdomDepartment of Medicine, University of Cambridge, United Kingdom; EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, United KingdomPurpose: To assess radiomics and deep learning (DL) methods in identifying symptomatic Carotid Artery Disease (CAD) from carotid CT angiography (CTA) images. We further compare the performance of these novel methods to the conventional calcium score. Methods: Carotid CT angiography (CTA) images from symptomatic patients (ischaemic stroke/transient ischaemic attack within the last 3 months) and asymptomatic patients were analysed. Carotid arteries were classified into culprit, non-culprit and asymptomatic. The calcium score was assessed using the Agatston method. 93 radiomic features were extracted from regions-of-interest drawn on 14 consecutive CTA slices. For DL, convolutional neural networks (CNNs) with and without transfer learning were trained directly on CTA slices. Predictive performance was assessed over 5-fold cross validated AUC scores. SHAP and GRAD-CAM algorithms were used for explainability. Results: 132 carotid arteries were analysed (41 culprit, 41 non-culprit, and 50 asymptomatic). For asymptomatic vs symptomatic arteries, radiomics attained a mean AUC of 0.96(± 0.02), followed by DL 0.86(± 0.06) and then calcium 0.79(± 0.08). For culprit vs non-culprit arteries, radiomics achieved a mean AUC of 0.75(± 0.09), followed by DL 0.67(± 0.10) and then calcium 0.60(± 0.02). For multi-class classification, the mean AUCs were 0.95(± 0.07), 0.79(± 0.05), and 0.71(± 0.07) for radiomics, DL and calcium, respectively. Explainability revealed consistent patterns in the most important radiomic features. Conclusions: Our study highlights the potential of novel image analysis techniques in extracting quantitative information beyond calcification in the identification of CAD. Though further work is required, the transition of these novel techniques into clinical practice may eventually facilitate better stroke risk stratification.http://www.sciencedirect.com/science/article/pii/S2352047724000492AIStrokeRadiomicsMachine learningCarotid arteryCoronary calcium |
| spellingShingle | Elizabeth P.V. Le Mark Y.Z. Wong Leonardo Rundo Jason M. Tarkin Nicholas R. Evans Jonathan R. Weir-McCall Mohammed M. Chowdhury Patrick A. Coughlin Holly Pavey Fulvio Zaccagna Chris Wall Rouchelle Sriranjan Andrej Corovic Yuan Huang Elizabeth A. Warburton Evis Sala Michael Roberts Carola-Bibiane Schönlieb James H.F. Rudd Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach European Journal of Radiology Open AI Stroke Radiomics Machine learning Carotid artery Coronary calcium |
| title | Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach |
| title_full | Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach |
| title_fullStr | Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach |
| title_full_unstemmed | Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach |
| title_short | Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach |
| title_sort | using machine learning to predict carotid artery symptoms from ct angiography a radiomics and deep learning approach |
| topic | AI Stroke Radiomics Machine learning Carotid artery Coronary calcium |
| url | http://www.sciencedirect.com/science/article/pii/S2352047724000492 |
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