105 Machine learning and carotid artery CT radiomics identify significant differences between culprit and non-culprit lesions in patients with stroke and transient ischaemic attack

• Published in: Heart (British Cardiac Society) Vol. 106; no. Suppl 2; pp. A82 - A84
• Main Authors: Le, Elizabeth Phuong Vi, Evans, Nicholas, Tarkin, Jason, Chowdhury, Mohammed, Zaccagna, Fulvio, Pavey, Holly, Wall, Chris, Huang, Yuan, Weir-McCall, Jonathan, Warburton, Elizabeth, Rundo, Leonardo, Schönlieb, Carola-Bibiane, Sala, Evis, Rudd, James HF
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group LTD 01.07.2020
• Subjects: Accuracy; Carotid arteries; Confidence intervals; Flow velocity; Machine learning; Neural networks; Radiomics; Stroke; Transient ischemic attack

IntroductionCarotid atherosclerosis is the main cause of ischaemic stroke. Texture analysis is a radiomic approach used to quantify image heterogeneity which can predict tumour aggression in oncology. We investigated whether this method could be applied to carotid artery disease to differentiate sym...