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

Journal: Npj imaging
Published Date: Dec 31, 2024

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 ( < 0.05) different across subgroups, while Fisher's tests revealed significant ( < 0.05) differences in the prevalence of several vascular risk factors across subgroup. Based on biomarker and clinical profiles, Subgroup 4 was characterized with neurodegeneration unrelated to CVD, Subgroup 3 identified patients with high CVD risk requiring aggressive intervention, and Subgroups 1, 2, and 5 identified patients with varying levels of moderate risk, suitable for long-term lifestyle interventions. This study supports personalized treatment and risk stratification based on FLAIR biomarkers.

Authors

  • Karissa Chan
    Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON Canada.
  • Corinne Fischer
    Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Network, Toronto, ON Canada.
  • Pejman Jabehdar Maralani
    Department of Medical Imaging, University of Toronto, Toronto, ON Canada.
  • Sandra E Black
    Institute of Medical Science, University of Toronto, Toronto, ON Canada.
  • Alan R Moody
    Department of Medical Imaging, University of Toronto, Toronto, ON Canada.
  • April Khademi
    Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON Canada.

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