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

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 patterns of biomarker evolution and stages within subgroups. The trained model was then applied to longitudinal MRIs. Stability of subtypes and stage change over time were assessed via Krippendorf's alpha and multilevel linear regression models, respectively. The prognostic relevance of SuStaIn classification was assessed with ordinal/logistic regression analyses. Results We selected 425 pwMS (35.9 +/- 9.9 years; F/M: 301/124), corresponding to 1129 MRI scans, along with healthy controls (N = 148; 35.9 +/- 13.0 years; F/M: 77/71) and external pwMS (N = 80; 40.4 +/- 11.9 years; F/M: 56/24) as reference populations. Based on 11 biomarkers surviving feature selection, two subtypes were identified, designated as deep gray matter (DGM)-first subtype (N = 238) and cortex-first subtype (N = 187) according to the atrophy pattern. Subtypes were consistent over time (alpha = 0.806), with significant annual stage increase (b = 0.20; p < 0.001). EDSS was associated with stage and DGM-first subtype (p <= 0.02). Baseline stage predicted long-term disability, transition to progressive course, and cognitive impairment (p <= 0.03), with the latter also associated with DGM-first subtype (p = 0.005). Conclusions Unsupervised learning modelling of brain MRI-derived volumetric features provides a biologically reliable and prognostically meaningful stratification of pwMS.

Automated summary

Using unsupervised machine learning, patients with multiple sclerosis (pwMS) were stratified based on brain MRI-derived volumetric features, providing reliable and predictive stratification of pwMS.