Assessment of carotid artery plaque components with machine learning classification using homodyned-K parametric maps and elastograms.

Quantitative ultrasound (QUS) imaging methods including elastography, echogenicity analysis and speckle statistical modeling are available from a single ultrasound radiofrequency data acquisition. Since these ultrasound imaging methods provide complementary quantitative tissue information, characterization of carotid artery plaques may gain from their combination. Sixty-six patients with symptomatic (n=26) and asymptomatic (n=40) carotid atherosclerotic plaques were included in the study. Of these, 31 underwent magnetic resonance imaging (MRI) to characterize plaque vulnerability and quantify plaque components. Ultrasound radiofrequency data sequence acquisitions were performed on all patients and were used to compute non-invasive vascular ultrasound elastography (NIVE) and other QUS features. Additional QUS features were computed from 3 types of images: homodyned-K (HK) parametric maps, Nakagami parametric maps, and log-compressed B-mode images. The following six classification tasks were performed: detection of 1) a small area of lipid; 2) a large area of lipid; 3) a large area of calcification; 4) presence of a ruptured fibrous cap; 5) differentiation of MRI-based classification of non-vulnerable carotid plaques from neovascularized or vulnerable ones; and 6) confirmation of symptomatic versus asymptomatic patients. Feature selection was firstly applied to reduce the number of QUS parameters to a maximum of 3 per classification task. A random forest machine learning algorithm was then used to perform classifications. Areas under receiver-operating curves (AUC) were computed with a bootstrap method. For all tasks, statistically significant higher AUCs were achieved with features based on elastography, HK parametric maps and B-mode gray levels, when compared to elastography alone or other QUS alone (p<0.001). For detection of a large area of lipid, the combination yielding the highest AUC (0.90, 95% CI 0.80 - 0.92, p<0.001) was based on elastography, HK and B-mode gray level features. To detect a large area of calcification, the highest AUC (0.95, 95% CI 0.94 - 0.96, p<0.001) was based on HK and B-mode gray level features. For other tasks, AUCs varied between 0.79 and 0.97. None of the best combinations contained Nakagami features. This study shows the added value of combining different features computed from a single ultrasound acquisition with machine learning to characterize carotid artery plaques.