{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:58:31Z","timestamp":1787320711359,"version":"build-2736575974"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"1158","license":[{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006662","name":"NIHR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018956","name":"Cambridge Biomedical Research Centre","doi-asserted-by":"publisher","award":["NIHR203312"],"award-info":[{"award-number":["NIHR203312"]}],"id":[{"id":"10.13039\/501100018956","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast MRI to improve upon conventional risk assessment models in determining culprit carotid artery lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). They underwent MRI within 2\u2009weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone (area under curve [AUC], 0.819\u2009\u00b1\u20090.002 vs 0.689\u2009\u00b1\u20090.019, respectively, P\u2009=\u2009.014). The radiomic model alone also provided value over the conventional model (AUC, 0.805\u2009\u00b1\u20090.003 vs 0.689\u2009\u00b1\u20090.019, respectively, P\u2009=\u2009.031). T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix, Grey Level Dependence Matrix, and Grey Level Size Zone Matrix sub-types were particularly useful in identifying textures which could detect vulnerable lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischaemic cerebrovascular events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>The clinical relevance of this work is that it addresses the need for a more comprehensive method of risk assessment for patients at risk of ischaemic stroke, beyond conventional stenosis measurement. This paper shows that in the case of carotid stroke, high-dimensional radiomics features can improve classification capabilities compared with stenosis measurement alone.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqae057","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:29:47Z","timestamp":1715041787000},"page":"1118-1124","source":"Crossref","is-referenced-by-count":10,"title":["Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability"],"prefix":"10.1093","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4087-5381","authenticated-orcid":false,"given":"Zakaria","family":"Meddings","sequence":"first","affiliation":[{"name":"Department of Radiology, University of Cambridge , Cambridge, CB2 0QQ, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo","family":"Rundo","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Cambridge , Cambridge, CB2 0QQ, United Kingdom"},{"name":"Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno , 84084 Fisciano SA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umar","family":"Sadat","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, Lister Hospital, East and North Hertfordshire NHS Trust , Stevenage, SG1 4AB, United Kingdom"},{"name":"Cambridge Mathematics of Information in Healthcare, University of Cambridge , Cambridge, CB3 0WA, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xihai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine , Beijing, 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongzhao","family":"Teng","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Cambridge , Cambridge, CB2 0QQ, United Kingdom"},{"name":"Tenoke Ltd , Cambridge, CB1 3RR, United Kingdom"},{"name":"Nanjing Jingsan Medical Science and Technology , Jiangsu, 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4327-3052","authenticated-orcid":false,"given":"Martin J","family":"Graves","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Cambridge , Cambridge, CB2 0QQ, United Kingdom"},{"name":"Department of Radiology, Addenbrooke\u2019s Hospital, Cambridge University Hospitals NHS Foundation Trust , Cambridge, CB2 0QQ, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,5,7]]},"reference":[{"key":"2024052917213864900_tqae057-B1","author":"World Health Organization. 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