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Updated: Jul 19, 2026
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics
J N P Zwemmer1, J Berkhof, J A Castelijns
1Department of Neurology, MS Center, VU University Medical Center, Amsterdam, The Netherlands. jnp.zwemmer@vumc.nl
Background:
Disease heterogeneity is a major issue in multiple sclerosis (MS). Classification of MS patients is usually based on clinical characteristics. More recently, a pathological classification has been presented. While clinical subtypes differ by magnetic resonance imaging (MRI) signature on a group level, a classification of individual MS patients based purely on MRI characteristics has not been presented so far.
Objectives:
To investigate whether a restricted classification of MS patients can be made based on a combination of quantitative and qualitative MRI characteristics and to test whether the resulting subgroups are associated with clinical and laboratory characteristics.
Methods:
MRI examinations of the brain and spinal cord of 50 patients were scored for 21 quantitative and qualitative characteristics. Using latent class analysis, subgroups were identified, for whom disease characteristics and laboratory measures were compared.
Results:
Latent class analysis revealed two subgroups that mainly differed in the extent of lesion confluency and MRI correlates of neuronal loss in the brain. Demographics and disease characteristics were comparable except for cognitive deficits. No correlations with laboratory measures were found.
Conclusions:
Latent class analysis offers a feasible approach for classifying subgroups of MS patients based on the presence of MRI characteristics. The reproducibility, longitudinal evolution and further clinical or prognostic relevance of the observed classification will have to be explored in a larger and independent sample of patients.
Insights
Magnetic resonance imaging (MRI) can classify multiple sclerosis (MS) patients into subgroups based on lesion confluency and neuronal loss. This MRI-based classification may help identify patients with cognitive deficits.
Area of Science:
- Neurology
- Radiology
- Biostatistics
Background:
- Multiple sclerosis (MS) exhibits significant disease heterogeneity, complicating patient classification.
- Current MS classification relies on clinical features, with pathological and group-level MRI signatures emerging.
- A method for classifying individual MS patients using only MRI characteristics is lacking.
Purpose of the Study:
- To determine if quantitative and qualitative MRI characteristics can classify MS patients into distinct subgroups.
- To assess associations between identified MRI-based subgroups and clinical/laboratory characteristics.
Main Methods:
- Brain and spinal cord MRI scans from 50 MS patients were analyzed for 21 quantitative and qualitative features.
- Latent class analysis (LCA) was employed to identify patient subgroups.
- Demographic, clinical, and laboratory data were compared across identified subgroups.
Main Results:
- LCA identified two MS patient subgroups primarily differentiated by lesion confluency and MRI evidence of neuronal loss.
- Subgroups showed comparable demographics and disease characteristics, except for cognitive deficits.
- No significant correlations were found between MRI-based subgroups and laboratory measures.
Conclusions:
- Latent class analysis provides a feasible method for subgrouping MS patients based on MRI characteristics.
- The identified MRI-based classification requires further validation in larger, independent cohorts for reproducibility and prognostic relevance.
