Quantitative Histogram Analysis on Intracranial Atherosclerotic Plaques: A High-Resolution Magnetic Resonance Imaging
Zhang Shi1,2, Jing Li1, Ming Zhao3
1Department of Radiology (Z.S., J. Li, W.P., T.J., Q.L., J. Lu), Changhai Hospital, Naval Medical University, Shanghai, China.
Insights
High-resolution MRI histogram features, particularly signal intensity dispersion (coefficient of variation), effectively differentiate culprit and nonculprit intracranial atherosclerosis plaques. These imaging biomarkers offer valuable insights beyond luminal stenosis for stroke risk assessment.
Area of Science:
- Neuroradiology
- Cerebrovascular Disease
- Biomarkers
Background:
- Intracranial atherosclerosis is a primary cause of stroke.
- High-resolution magnetic resonance imaging (HR-MRI) offers valuable imaging biomarkers for ischemic event risk.
- Understanding plaque characteristics is crucial for stroke prevention.
Purpose of the Study:
- To evaluate differences in histogram features between culprit and nonculprit intracranial atherosclerosis using HR-MRI.
- To identify key imaging determinants differentiating plaque types.
- To assess the predictive value of histogram features for stroke risk.
Main Methods:
- Recruited 247 patients with intracranial atherosclerosis undergoing sequential HR-MRI.
- Analyzed quantitative plaque features: stenosis, burden, minimum luminal area, intraplaque hemorrhage, enhancement ratio, and coefficient of variation (COV).
- Utilized stepwise regression analysis to identify differentiating features and calculate odds ratios (ORs).
Main Results:
- 190 plaques analyzed; 88 in middle cerebral artery (MCA), 102 in basilar artery (BA).
- Intraplaque hemorrhage, minimum luminal area, and COV significantly predicted culprit plaques in MCA.
- Enhancement ratio, intraplaque hemorrhage, and COV predicted plaque type in BA.
- COV demonstrated high sensitivity (0.79), specificity (0.80), and accuracy (0.80) in defining plaque type for both arteries.
Conclusions:
- HR-MRI histogram features provide complementary value to luminal stenosis in classifying intracranial atherosclerosis lesions.
- Dispersion of signal intensity (COV) is a highly effective predictive parameter for differentiating plaque types.
- These findings enhance the understanding of stroke mechanisms and risk stratification.
Background And Purpose:
Intracranial atherosclerosis is one of the main causes of stroke, and high-resolution magnetic resonance imaging provides useful imaging biomarkers related to the risk of ischemic events. This study aims to evaluate differences in histogram features between culprit and nonculprit intracranial atherosclerosis using high-resolution magnetic resonance imaging.
Methods:
Two hundred forty-seven patients with intracranial atherosclerosis who underwent high-resolution magnetic resonance imaging sequentially between January 2015 and December 2016 were recruited. Quantitative features, including stenosis, plaque burden, minimum luminal area, intraplaque hemorrhage, enhancement ratio, and dispersion of signal intensity (coefficient of variation), were analyzed based on T2-, T1-, and contrast-enhanced T1-weighted images. Step-wise regression analysis was used to identify key determinates differentiating culprit and nonculprit plaques and to calculate the odds ratios (ORs) with 95% CIs.
Results:
In total, 190 plaques were identified, of which 88 plaques (37 culprit and 51 nonculprit) were located in the middle cerebral artery and 102 (57 culprit and 45 nonculprit) in the basilar artery. Nearly 90% of culprit lesions had a degree of luminal stenosis of <70%. Multiple logistic regression analyses showed that intraplaque hemorrhage (OR, 16.294 [95% CI, 1.043-254.632]; P=0.047), minimum luminal area (OR, 1.468 [95% CI, 1.032-2.087]; P=0.033), and coefficient of variation (OR, 13.425 [95% CI, 3.987-45.204]; P<0.001) were 3 significant features in defining culprit plaques in middle cerebral artery. The enhancement ratio (OR, 9.476 [95% CI, 1.256-71.464]; P=0.029), intraplaque hemorrhage (OR, 2.847 [95% CI, 0.971-10.203]; P=0.046), and coefficient of variation (OR, 10.068 [95% CI, 2.820-21.343]; P<0.001) were significantly associated with plaque type in basilar artery. Coefficient of variation was a strong independent predictor in defining plaque type for both middle cerebral artery and basilar artery with sensitivity, specificity, and accuracy being 0.79, 0.80, and 0.80, respectively.
Conclusions:
Features characterized by high-resolution magnetic resonance imaging provided complementary values over luminal stenosis in defined lesion type for intracranial atherosclerosis; the dispersion of signal intensity in histogram analysis was a particularly effective predictive parameter.
