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

Abstract 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...

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Autores principales:	Elizabeth P. V. Le, Leonardo Rundo, Jason M. Tarkin, Nicholas R. Evans, Mohammed M. Chowdhury, Patrick A. Coughlin, Holly Pavey, Chris Wall, Fulvio Zaccagna, Ferdia A. Gallagher, Yuan Huang, Rouchelle Sriranjan, Anthony Le, Jonathan R. Weir-McCall, Michael Roberts, Fiona J. Gilbert, Elizabeth A. Warburton, Carola-Bibiane Schönlieb, Evis Sala, James H. F. Rudd
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3c38d77b56a846ac9f44b3d1df1785f4
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spelling	oai:doaj.org-article:3c38d77b56a846ac9f44b3d1df1785f42021-12-02T13:30:28ZAssessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events10.1038/s41598-021-82760-w2045-2322https://doaj.org/article/3c38d77b56a846ac9f44b3d1df1785f42021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82760-whttps://doaj.org/toc/2045-2322Abstract 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.Elizabeth P. V. LeLeonardo RundoJason M. TarkinNicholas R. EvansMohammed M. ChowdhuryPatrick A. CoughlinHolly PaveyChris WallFulvio ZaccagnaFerdia A. GallagherYuan HuangRouchelle SriranjanAnthony LeJonathan R. Weir-McCallMichael RobertsFiona J. GilbertElizabeth A. WarburtonCarola-Bibiane SchönliebEvis SalaJames H. F. RuddNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Elizabeth P. V. Le Leonardo Rundo Jason M. Tarkin Nicholas R. Evans Mohammed M. Chowdhury Patrick A. Coughlin Holly Pavey Chris Wall Fulvio Zaccagna Ferdia A. Gallagher Yuan Huang Rouchelle Sriranjan Anthony Le Jonathan R. Weir-McCall Michael Roberts Fiona J. Gilbert Elizabeth A. Warburton Carola-Bibiane Schönlieb Evis Sala James H. F. Rudd Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
description	Abstract 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.
format	article
author	Elizabeth P. V. Le Leonardo Rundo Jason M. Tarkin Nicholas R. Evans Mohammed M. Chowdhury Patrick A. Coughlin Holly Pavey Chris Wall Fulvio Zaccagna Ferdia A. Gallagher Yuan Huang Rouchelle Sriranjan Anthony Le Jonathan R. Weir-McCall Michael Roberts Fiona J. Gilbert Elizabeth A. Warburton Carola-Bibiane Schönlieb Evis Sala James H. F. Rudd
author_facet	Elizabeth P. V. Le Leonardo Rundo Jason M. Tarkin Nicholas R. Evans Mohammed M. Chowdhury Patrick A. Coughlin Holly Pavey Chris Wall Fulvio Zaccagna Ferdia A. Gallagher Yuan Huang Rouchelle Sriranjan Anthony Le Jonathan R. Weir-McCall Michael Roberts Fiona J. Gilbert Elizabeth A. Warburton Carola-Bibiane Schönlieb Evis Sala James H. F. Rudd
author_sort	Elizabeth P. V. Le
title	Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
title_short	Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
title_full	Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
title_fullStr	Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
title_full_unstemmed	Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
title_sort	assessing robustness of carotid artery ct angiography radiomics in the identification of culprit lesions in cerebrovascular events
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3c38d77b56a846ac9f44b3d1df1785f4
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Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events

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