Evaluation of carotid plaque vulnerability in vivo: Correlation between dynamic contrast-enhanced MRI and MRI-modified AHA classification

MR Imaging of Carotid Artery Atherosclerosis: Updated Evidence on High-Risk Plaque Features and Emerging Trends

MR imaging is well-established as the criterion standard for carotid artery atherosclerosis imaging. The capability of MR imaging to differentiate numerous plaque components has been demonstrated, including those features that are associated with a high risk of sudden changes, thrombosis, or embolization. The field of carotid plaque MR imaging is constantly evolving, with continued insight into the imaging appearance and implications of various vulnerable plaque characteristics. This article will review the most up-to-date knowledge of these high-risk plaque features on MR imaging and will delve into 2 major emerging topics: the role of vulnerable plaques in cryptogenic strokes and the potential use of MR imaging to modify carotid endarterectomy treatment guidelines.

Choice of imaging to evaluate carotid stenosis and guide management

Carotid artery disease is a cause of ischemic stroke and is associated with cognitive decline. Besides the evaluation of the degree of stenosis, it is also crucial to assess the morphology of the atherosclerotic plaque, for a prompt and accurate diagnosis, and to make the best decision for the patient. On top of noninvasive duplex ultrasound (DUS) and invasive digital subtraction angiography (DSA), compute tomography angiography (CTA) and magnetic resonance angiography (MRA) are often used effectively as noninvasive imaging tools to study carotid stenoses. This review describes the fundamental characteristics of carotid artery plaques, and how they can be best evaluated with currently available imaging methods.

Evaluation of non-stenotic carotid atherosclerotic plaques with combined FDG-PET imaging and CT angiography in patients with ischemic stroke of unknown origin

OBJECTIVES

Non-stenotic plaques are an underestimated cause of ischemic stroke. Imaging aspects of high-risk carotid plaques can be identified on CT angiography (CTA) and 18F-fluoro-deoxyglucose positron emission tomography (FDG-PET) imaging. We evaluated in patients with cryptogenic ischemic stroke the usefulness of FDG-PET-CTA.

METHODS

44 patients imaged with CTA and FDG-PET were identified retrospectively. Morphological features were identified on CTA. Intensity of FDG uptake in carotid arteries was quantified on PET.

RESULTS

Patients were imaged 7 ± 8 days after stroke. 44 non-stenotic plaques with increased 18F-FDG uptake were identified in the carotid artery ipsilateral to stroke and 7 contralateral. Most-diseased-segment TBR on FDG-PET was higher in artery ipsilateral vs. contralateral to stroke (2.24 ± 0.80 vs. 1.84 ± 0.50; p < .05). In the carotid region with high FDG uptake, prevalence of hypodense plaques and extent of hypodensity on CTA were higher in artery ipsilateral vs. contralateral to stroke (41% vs. 11%; 0.72 ± 1.2 mm2 vs. 0.13 ± 0.43 mm2; p < .05).

CONCLUSIONS

In patients with ischemic stroke of unknown origin and non-stenotic plaques, we found an increased prevalence of high-risk plaques features ipsilateral vs. contralateral to stroke on FDG-PET-CTA imaging suggesting a causal role for these plaques.

Elevated Hemoglobin A1c Is Associated With Leaky Plaque Neovasculature as Detected by Dynamic Contrast-Enhanced Magnetic Resonance Imaging

BACKGROUND

Patients with diabetes have accelerated atherosclerosis progression, but the underlying mechanisms are not fully understood. Dynamic contrast-enhanced magnetic resonance imaging has allowed in vivo characterization of plaque neovasculature, which plays a critical role in plaque progression. We aimed to evaluate the impact of diabetes on carotid plaque neovasculature as assessed by dynamic contrast-enhanced magnetic resonance imaging.

METHODS

Patients with recent ischemic stroke and ipsilateral carotid plaque underwent multicontrast magnetic resonance imaging for characterizing plaque morphology and dynamic contrast-enhanced magnetic resonance imaging for pharmacokinetic parameters of plaque neovasculature, including transfer constant (Ktrans, reflecting flow, endothelial surface area, and permeability) and fractional plasma volume (νp).

RESULTS

Sixty-five patients were enrolled, including 30 patients with diabetes (years since diagnosis: median 5.0 [interquartile range, [3.0-12.0]) and 35 patients without diabetes. Subjects with diabetes had a greater plaque burden and a higher prevalence of high-risk characteristics. Additionally, carotid plaques in the subjects with diabetes showed higher Ktrans than those in the subjects without diabetes (0.100±0.048 min-1 versus 0.067±0.042 min-1, P=0.005) but νp was numerically lower in the subjects with diabetes (5.2±3.7% versus 6.2±4.3%, P=0.31). The association of diabetes with high Ktrans (β=0.033, P=0.005) was independent of patient and plaque characteristics and remained largely intact after adjusting for serum lipids, glucose, or hs-CRP (high-sensitivity C-reactive protein). However, it became nonexistent after adjusting for hemoglobin A1c (β=-0.010, P=0.49).

CONCLUSIONS

Dynamic contrast-enhanced magnetic resonance imaging of carotid plaques suggested that plaque neovasculature in patients with diabetes is leaky, indicating enhanced capability of bringing blood constituents and facilitating extravasation of inflammatory cells, erythrocytes, and plasma proteins. Leaky plaque neovasculature correlated with hemoglobin A1c and may play a role in accelerated atherosclerosis progression in diabetes.

Elastography ultrasound with machine learning improves the diagnostic performance of traditional ultrasound in predicting kidney fibrosis

BACKGROUND

Noninvasively predicting kidney tubulointerstitial fibrosis is important because it's closely correlated with the development and prognosis of chronic kidney disease (CKD). Most studies of shear wave elastography (SWE) in CKD were limited to non-linear statistical dependencies and didn't fully consider variables' interactions. Therefore, support vector machine (SVM) of machine learning was used to assess the prediction value of SWE and traditional ultrasound techniques in kidney fibrosis.

METHODS

We consecutively recruited 117 CKD patients with kidney biopsy. SWE, B-mode, color Doppler flow imaging ultrasound and hematological exams were performed on the day of kidney biopsy. Kidney tubulointerstitial fibrosis was graded by semi-quantification of Masson staining. The diagnostic performances were accessed by ROC analysis.

RESULTS

Tubulointerstitial fibrosis area was significantly correlated with eGFR among CKD patients (R = 0.450, P < 0.001). AUC of SWE, combined with B-mode and blood flow ultrasound by SVM, was 0.8303 (sensitivity, 77.19%; specificity, 71.67%) for diagnosing tubulointerstitial fibrosis (>10%), higher than either traditional ultrasound, or SWE (AUC, 0.6735 [sensitivity, 67.74%; specificity, 65.45%]; 0.5391 [sensitivity, 55.56%; specificity, 53.33%] respectively. Delong test, p < 0.05); For diagnosing different grades of tubulointerstitial fibrosis, SWE combined with traditional ultrasound by SVM, had AUCs of 0.6429 for mild tubulointerstitial fibrosis (11%-25%), and 0.9431 for moderate to severe tubulointerstitial fibrosis (>50%), higher than other methods (Delong test, p < 0.05).

CONCLUSION

SWE with SVM modeling could improve the diagnostic performance of traditional kidney ultrasound in predicting different kidney tubulointerstitial fibrosis grades among CKD patients.