Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach.

ABSTRACT:

Objectives

To stratify patients with multiple sclerosis (pwMS) based on brain MRI-derived volumetric features using unsupervised machine learning.

Methods

The 3-T brain MRIs of relapsing-remitting pwMS including 3D-T1w and FLAIR-T2w sequences were retrospectively collected, along with Expanded Disability Status Scale (EDSS) scores and long-term (10 ± 2 years) clinical outcomes (EDSS, cognition, and progressive course). From the MRIs, volumes of demyelinating lesions and 116 atlas-defined gray matter regions were automatically segmented and expressed as z-scores referenced to external populations. Following feature selection, baseline MRI-derived biomarkers entered the Subtype and Stage Inference (SuStaIn) algorithm, which estimates subgroups characterized by distinct pattern

SUBMITTER: Pontillo G 

PROVIDER: S-EPMC9279232 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature