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 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

SUBMITTER: Le EPV 

PROVIDER: S-EPMC7876096 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature