Association of intracranial atherosclerotic plaque features with total cerebral small vessel disease burden: a retrospective study in branch atheromatous disease.

Abstract

Objective: We investigated the association between middle cerebral artery atherosclerotic plaque features and cerebral small vessel disease (CSVD) imaging markers as well as the total CSVD burden, in patients with branch atheromatous disease (BAD).

Methods: Plaque parameters were quantified using high-resolution magnetic resonance imaging (HR-MRI) with ImageJ software, to characterize distribution, lumen stenosis, remodeling patterns, and other relevant features. Conventional MRI assessed CSVD imaging markers and total CSVD burden. Multivariate logistic regression analysis was performed following adjustment for potential confounders. Receiver operating characteristic (ROC) curve analysis with the DeLong test assessed the predictive value of plaque features for total CSVD burden.

Results: Compared with the non-plaque group, the plaque group showed significantly higher proportions of severe white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), and moderate-to-severe CSVD burden (p < 0.05). In multivariate analysis, the presence of plaque was an independent risk factor for WMHs (OR = 2.920), CMBs (OR = 1.995), and moderate-to-severe CSVD burden (OR = 2.853); plaque distribution was an independent risk factor for WMHs (OR = 3.367); eccentric plaques were independent risk factors for lacunar infarction (OR = 8.670) and CMBs (OR = 7.891); positive remodeling (OR = 9.285) and eccentric plaques (OR = 10.355) were independent risk factors for moderate-to-severe CSVD burden. ROC analysis demonstrated plaque vulnerability effectively predicted moderate-to-severe CSVD burden (AUC = 0.8808, p < 0.05).

Conclusion: In ischemic stroke patients, distinct intracranial atherosclerotic stenosis (ICAS) plaque features correlate with specific CSVD phenotypes. Vulnerable plaques not only significantly increase total CSVD burden but also effectively predict CSVD severity. These findings elucidate how ICAS influences CSVD burden progression from an HR-MRI perspective and facilitate clinical risk stratification.