Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers.

ABSTRACT: This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAIR biomarkers. Intensity and texture biomarkers were extracted from FLAIR volumes of 379 atherosclerosis patients. K-Means clustering identified five homogeneous subgroups. The 15 most important biomarkers for subgroup differentiation, identified via Random Forest classification, were used to generate biomarker profiles. ANOVA tests showed age and white matter lesion volume were significantly (p < 0.05) different across subgroups, while Fisher's tests revealed significant (p < 0.05) differences in the prevalence of several vascular risk factors across subgroup. Based on biomarker and clinical profiles, Subgroup 4 was characterize

SUBMITTER: Chan K 

PROVIDER: S-EPMC11688236 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature