Artificial Intelligence analysis of lesion dynamics and brain volume in patients with multiple sclerosis

Quantitative MRI markers increasingly complement conventional clinical assessment in multiple sclerosis (MS). Artificial intelligence (AI)-based volumetry enables standardized evaluation of lesion burden and brain atrophy in routine care.

To examine the association between AI-derived volumetric measures and disability, assess whether annual brain volume loss (ABVL) and lesion dynamics predict atrophy, and descriptively compare proxy radiological phenotype groups with established clinical phenotypes in a real-world MS cohort.

This retrospective study included 888 MRI examinations from 455 patients with MS (2016-2020). Longitudinal analyses were performed in 234 patients with ≥2 scans (667 MRIs), and an early MS cohort comprised 302 patients (580 scans). Automated segmentation (mdbrain® v3.4.0) provided lesion metrics and age/sex/skull-volume-adjusted brain volumes from routine 3D FLAIR and native T1-weighted sequences acquired under real-world clinical conditions. Pathological atrophy was defined as a normative z-score < -2. Generalized estimating equations (GEE) evaluated predictors of atrophy.

Proxy radiological phenotype groups were defined as lesion-led (43.2%), cortex-led (35.4%), and NAWM-led (21.4%); clinical phenotypes included RRMS (82%), SPMS (14%), and PPMS (4%). EDSS correlated with lesion volume (ρ=0.28, p<.001) and total brain volume (ρ=-0.32, p<.001). In 433 longitudinal intervals, 11.8% showed mdbrain-defined atrophy. Higher EDSS (OR 1.53, 95% CI 1.28-1.83, p<.001) and longer follow-up (OR 2.24, 95% CI 1.36-3.70, p=.001) independently predicted atrophy; ABVL showed only borderline significance (p=.071). Lesion dynamics were not independently predictive of atrophy (p>.60). ABVL alone showed low discriminative value (AUC 0.571), whereas EDSS + interval length achieved AUC 0.766.

Gray-matter-predominant atrophy correlated more strongly with disability than lesion burden and frequently occurred in the absence of new lesions, indicating lesion-independent neurodegenerative processes that were observed across the defined proxy radiological phenotype groups. AI-based quantitative MRI offers reproducible atrophy assessment in real-world practice and may support quantitative MRI-based monitoring frameworks that include brain volume loss and facilitate detection of subclinical progression.