Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
Creators: name: Le, Elizabeth P. V.
name: Rundo, Leonardo
name: Tarkin, Jason M.
name: Evans, Nicholas R.
name: Chowdhury, Mohammed M.
name: Coughlin, Patrick A.
name: Pavey, Holly
name: Wall, Chris
name: Zaccagna, Fulvio
name: Gallagher, Ferdia A.
name: Huang, Yuan
name: Sriranjan, Rouchelle
name: Le, Anthony
name: Weir-McCall, Jonathan R.
name: Roberts, Michael
name: Gilbert, Fiona J.
name: Warburton, Elizabeth A.
name: Schönlieb, Carola-Bibiane
name: Sala, Evis
name: Rudd, James H. F.
Publisher: name: Apollo - University of Cambridge Repository
Year: 2021
Subjects: Article
/692/308
/692/700/1421
/692/4019/592/75/593/2100
/692/699/75/593/1353
article

description: Funder: School of Clinical Medicine, University of Cambridge; doi: http://dx.doi.org/10.13039/501100007552
description: Funder: Frank Edward Elmore Fund
description: Funder: National Institute for Health Research (NIHR) Imperial Biomedical Research Centre
description: Funder: British Heart Foundation Cambridge Centre of Research Excellence
description: Funder: Royal College of Surgeons of England; doi: http://dx.doi.org/10.13039/501100000297
description: Funder: Cancer Research UK; doi: http://dx.doi.org/10.13039/501100000289
description: Funder: AstraZeneca Oncology R
description: Funder: National Institute for Health Research; doi: http://dx.doi.org/10.13039/501100000272
description: Funder: Leverhulme Trust; doi: http://dx.doi.org/10.13039/501100000275
description: Funder: Cantab Capital Institute for the Mathematics of Information
description: Funder: Alan Turing Institute; doi: http://dx.doi.org/10.13039/100012338
description: Funder: NIHR Cambridge Biomedical Research Centre
description: Funder: Higher Education Funding Council for England; doi: http://dx.doi.org/10.13039/501100000384
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.