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. (17th July 2020)

• Record Type: Journal Article
• 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
• Is Part Of: Heart. Volume 106(2020)Supplement 2
• Page Start: A82
• Page End: A84
• Publication Date: 2020-07-17
• Subjects: CT radiomics -- machine learning -- stroke; Heart -- Diseases -- Treatment -- Periodicals; Cardiology -- Periodicals; 616.12
• DOI: 10.1136/heartjnl-2020-BCS.105
• Languages: English
• ISSNs: 1355-6037

Abstract

Abstract : Introduction: Carotid 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 symptomatic from asymptomatic patients and culprit from non-culprit plaques, and then whether machine learning (ML) could correctly classify plaques based on these features. Methods: CT angiography (CTA) images from symptomatic patients with carotid artery-related cerebrovascular accidents (CVAs) and from asymptomatic (ASX) patients were studied. Regions-of-interest (ROIs) were drawn on 14 consecutive carotid artery CTA slices with 3mm slice thickness. PyRadiomics was used for isotropic image (1x1x1) resampling and normalisation prior to texture feature extraction from 6 different classes (Table 1 ). Asymptomatic carotids were compared to culprit carotids (CC), and non-culprit (NC) carotids using the Mann Whitney U test or Wilcoxon signed-rank tests as appropriate, with a p-value <0.0005 deemed statistically significant after Bonferroni correction. Non-normally distributed variables are reported as median (interquartile range). To assess the discriminatory ability of radiomic features in multi-class classification (ASX, CC or NC), texture features were fed into a Python scikit-learn pipeline for feature selection with variance thresholding, feature scaling and dimensionality reduction incorporated within a 5-fold cross validation scheme with 5 different ML classifiers to reduce the risk of data leakage and overfitting. Mean cross validation (CV) accuracy, area under the receiver operating curve (AUC) and 95% confidence intervals (CI) are reported. Results: The dataset comprised 82 carotid arteries from 41 symptomatic patients (41 culprit; 41 non-culprit) and 50 carotid arteries from 25 asymptomatic patients. CC and NC carotids showed significant differences in both first- and second-order features (IH Median: CC 618 (61); NC 646 (97), p<0.005) and (GLDM Large Dependence High Grey-Level Emphasis: CC 3147 (1837), NC 4811 (2181), p<0.0001), respectively. Both CC and NC carotids had higher heterogeneity than asymptomatic carotids (GLDM Dependence Entropy: CC 6.59 (0.43), NC 6.57 (0.52), ASX 6.24 (0.26), p<0.0001). All ML classifiers performed better than a randomly guessing classifier (mean accuracy 33.3%) in this multi-class (n=3) classification task (Table 2 ; Figure 2 ), with the neural network achieving the highest accuracy of 69%, CI [61%, 77%] with AUC 0.82 CI [0.78, 0.86]. Conclusions: Textural analysis combined with machine learning on carotid CT scans reveals highly significant differences between symptomatic and asymptomatic patients, and between culprit and non-culprit carotid arteries within symptomatic patients. This approach could help identify patients at high-risk of stroke for aggressive medical therapy and surveillance. Conflict of Interest: None