Search

Background Carotid artery atherosclerosis is an important cause of ischaemic stroke. In oncology, textural analysis (“radiomics”) of computed tomography (CT) images can predict the metastatic potential and prognosis of several types of malignant tumours. We investigated whether this quantitative approach could be applied in carotid artery disease. Purpose (1) To evaluate the feasibility of computed tomography angiography (CTA) texture analysis in differentiating symptomatic from asymptomatic patients. (2) To investigate whether CTA carotid texture analysis can identify culprit lesions in patients with stroke and transient ischaemic attack (TIA). Methods Carotid CTAs of consented research subjects were included in the study. Symptomatic patients had confirmed carotid artery-related ischaemic stroke or TIA in the 7 days before CTA imaging. Asymptomatic (ASX) patients had no prior stroke/TIA. Both TexRAD, a research texture analysis software, and PyRadiomics, a Python package for radiomics studies, were used to extract 99 first-order and higher-order texture features from regions-of-interest (ROI) drawn around the outer wall of the carotid artery. Single-slice analysis compared the carotid bifurcations of symptomatic and asymptomatic patients, and of culprit (CC) and non-culprit (NC) arteries in symptomatic patients. Multi-slice analysis was conducted using a 3D volume defined by ROIs drawn on 14 consecutive CT slices of 3mm thickness, covering 3cm of carotid artery. The Mann-Whitney U test was used for inter-subject comparisons (ASX vs CC; ASX vs NC) and the Wilcoxon signed-rank test was used for intra-subject comparisons (CC vs NC). A p value Results The dataset comprised 82 carotid arteries from 41 symptomatic patients (41 culprit; 41 non-culprit) and 50 carotid arteries from 25 asymptomatic patients. Single-slice analysis revealed greater homogeneity in asymptomatic carotids versus symptomatic culprit carotids (Uniformity: ASX 0.11 (0.05); CC 0.08 (0.05), p Conclusion Textural analysis of carotid CTAs reveal significant differences between symptomatic and asymptomatic patients and between culprit and non-culprit carotid arteries within symptomatic patients. This approach could be used to identify patients at high risk of further stroke for aggressive medical therapy and surveillance. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): EPVL is undertaking a PhD funded by the Cambridge School of Clinical Medicine and the Medical Research Council's Doctoral Training Partnership

Background Convolutional neural networks (CNNs), part of deep learning, are used widely for computer vision tasks and in some medical domains, such as mammography interpretation. The application of deep learning to carotid artery imaging is scarce. We investigated the ability of deep learning to correctly classify contrast CT images of the carotid arteries without the need for prior feature selection. Purpose (1) To assess the ability of deep learning to differentiate symptomatic patients (had prior stroke or transient ischaemic attack [TIA]) from asymptomatic patients (no prior stroke/TIA) using contrast CT scans alone. (2) To investigate whether deep learning can further discriminate between culprit and non-culprit carotid arteries in symptomatic patients. (3) To assess the interpretability of the deep learning models. Methods Carotid contrast CT scans of consented research subjects were included in the study. Symptomatic patients had confirmed carotid artery-related ischaemic stroke or TIA in the 7 days before CT imaging, and asymptomatic patients had no prior cerebrovascular events. The dataset comprised 1148 axial symptomatic slices (covering a 3cm area of each carotid artery in 41 patients; 41 culprit and 41 non-culprit carotids) and 700 asymptomatic slices (from the bilateral carotid arteries of 25 patients). The dataset was split such that 75% was used for training and 25% for testing. A 30x30 bounding box was used to create patches of the carotid arteries from these axial slices for use as input to the CNN, a modified VGG16 architecture initialised with ImageNet weights to leverage transfer learning (the application of a model trained in one domain to a different domain) implemented in Python. Data augmentation was applied to the training set and the model was trained for 100 epochs using a cyclic learning rate, the RMSProp optimizer and binary cross-entropy loss. Class activation heatmaps were generated using the GradCAM method to highlight the areas of the image that were most important to the model for making its classification decision. Results The deep learning model was 92% accurate in correctly identifying carotid arteries from symptomatic patients versus those from asymptomatic patients. Discriminating between culprit versus non-culprit carotid arteries in symptomatic patients alone was 71% accurate. The class activation heatmaps demonstrated how the model learnt to localise the carotid artery within the image patch, and to ignore the arterial lumen when making its classification decision. Conclusions Deep learning can be used to differentiate between symptomatic and asymptomatic carotid CT scans from stroke/TIA subjects without the need for prior feature engineering. The model learns to identify relevant features in the image that predict the patients' symptom state. If further validated, this approach could be used to identify high-risk patients for intensive medical therapy. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): EPVL is undertaking a PhD funded by the Cambridge School of Clinical Medicine and the Medical Research Council's Doctoral Training Partnership