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Quantitative score of the vessel morphology in middle cerebral artery atherosclerosis
Yao Meng1, Mingli Li2, Yannan Yu1
1Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Insights
Quantitative analysis of middle cerebral artery (MCA) atherosclerosis plaque morphology accurately distinguishes between symptomatic and asymptomatic plaque types. A novel Symptomatic Plaque Score (SPS) effectively predicts plaque type using high-resolution magnetic resonance imaging (HRMRI) data.
Area of Science:
- Neurology
- Radiology
- Cardiovascular Research
Background:
- Middle cerebral artery (MCA) atherosclerosis is a significant cause of stroke.
- Accurate characterization of plaque morphology is crucial for understanding stroke risk.
- Current methods for plaque typing may lack sufficient discriminatory power.
Purpose of the Study:
- To quantitatively assess middle cerebral artery (MCA) vessel morphology in atherosclerosis.
- To explore the value of morphological features in discriminating between symptomatic and asymptomatic plaque types.
- To develop and validate a predictive model for plaque type classification.
Main Methods:
- High-resolution magnetic resonance imaging (HRMRI) was used to analyze MCA morphology in patients with MCA stenosis (>50%).
- Quantitative measurements included lumen area, wall area, plaque length, and eccentricity.
- Firth's penalized logistic regression was employed to develop a Symptomatic Plaque Score (SPS) model, which was then prospectively validated.
Main Results:
- Four factors significantly associated with symptomatic plaques were identified: stenotic lumen area (≥2.28 mm²), stenotic wall area (≥8.88 mm²), plaque length, and plaque eccentricity.
- The developed SPS model demonstrated strong diagnostic ability with an area under the curve (AUC) of 0.890 in the construction cohort and 0.862 in the validation cohort.
- The pooled analysis of 235 plaques established an optimal cutoff score (SPS ≥ 2.79) with 81.1% sensitivity and 80.5% specificity for discriminating symptomatic from asymptomatic plaques.
Conclusions:
- Quantitative analysis of MCA morphology provides an independent and accurate method for discriminating plaque types.
- The findings suggest a strong link between vessel morphology and the underlying pathophysiology of MCA atherosclerosis.
- Further research is needed to confirm the clinical utility of the SPS model in monitoring plaque progression and assessing vulnerability.
Background:
We aimed to quantitatively assess the vessel morphology of middle cerebral artery (MCA) atherosclerosis and explore its value in discriminating plaque types.
Methods:
Patients were selected from a high-resolution magnetic resonance imaging (HRMRI) study from January 2007 to December 2015. One hundred and three patients with acute cerebral infarcts due to MCA stenosis (>50%) and eighty-nine patients with asymptomatic MCA stenosis (>50%) were included. Quantitative measurements of MCA morphology, including lumen area, outer-wall and wall area at stenotic site and reference site, stenotic degree, plaque length, remodeling index and plaque eccentricity, were performed on HRMRI with observers blinded to clinical presentations. Firth's penalized logistic regression analysis was used to construct a symptomatic plaque score (SPS) model. Then, the HRMRI data of 39 patients prospectively enrolled from January 2016 to January 2017 were used to validate the SPS model.
Results:
The HRMRI data of 103 patients with symptomatic MCA stenosis and 89 patients with asymptomatic MCA stenosis in the construction cohort were analyzed. Four main factors were found to be associated with symptomatic plaques: stenotic lumen area ≥ 2.28 mm2, stenotic wall area ≥ 8.88 mm2, plaque length and presence of an eccentric plaque. Summation of each logistic regression coefficient multiplying the corresponding score produced the SPS with an area under curve (AUC) of 0.890 on receiver operating characteristics analysis. Validation of the score of 39 plaques (19 symptomatic and 20 asymptomatic) revealed an AUC of 0.862, confirming the continued diagnostic ability. When the data were pooled in all 235 plaques, the optimal cutoff score of discriminating symptomatic and asymptomatic plaques was 2.79 (SPS ≥ 2.79 indicating a symptomatic plaque) with AUC = 0.886, sensitivity 81.1% and specificity 80.5%.
Conclusions:
The quantitative analysis of MCA morphology can independently and accurately discriminate plaque types, suggesting its close association with the underlying pathophysiology. Further prospective studies are required to verify whether the SPS model is clinically valuable in monitoring plaque progression and assessing the vulnerability.
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