Rationale and objectives: Patients presenting with transient ischemic attack or stroke may have symptom-related lesions on acute computed tomography angiography (CTA) such as free-floating intraluminal thrombus (FFT). It is difficult to distinguish FFT from carotid plaque, but the distinction is critical as management differs. By contouring the shape of these vascular lesions ("virtual endarterectomy"), advanced morphometric analysis can be performed. The objective of our study is to determine whether quantitative shape analysis can accurately differentiate FFT from atherosclerotic plaque.
Materials and methods: We collected 23 consecutive cases of suspected carotid FFT seen on CTA (13 men, 65 ± 10 years; 10 women, 65.5 ± 8.8 years). True-positive FFT cases (FFT+) were defined as filling defects resolving with anticoagulant therapy versus false-positives (FFT-), which remained unchanged. Lesion volumes were extracted from CTA images and quantitative shape descriptors were computed. The five most discriminative features were used to construct receiver operator characteristic (ROC) curves and to generate three machine-learning classifiers. Average classification accuracy was determined by cross-validation.
Results: Follow-up imaging confirmed sixteen FFT+ and seven FFT- cases. Five shape descriptors delineated FFT+ from FFT- cases. The logistic regression model produced from combining all five shape features demonstrated a sensitivity of 87.5% and a specificity of 71.4% with an area under the ROC curve = 0.85 ± 0.09. Average accuracy for each classifier ranged from 65.2%-76.4%.
Conclusions: We identified five quantitative shape descriptors of carotid FFT. This shape "signature" shows potential for supplementing conventional lesion characterization in cases of suspected FFT.
Keywords: CT angiography; computer aided diagnosis; free-floating thrombus; image postprocessing; stroke.
Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.