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Differences in Brain Atrophy Pattern between People with Multiple Sclerosis and Systemic Diseases with Central
Małgorzata Siger1, Jacek Wydra2, Paula Wildner1
1Department of Neurology, Medical University of Lodz, Kopcinskiego Street 22, 90-414 Lodz, Poland.
Journal of Clinical Medicine
|January 23, 2024
Summary
Brain MRI analysis using two-dimensional linear measurements (2DLMs) can differentiate multiple sclerosis (MS) from systemic diseases with central nervous system involvement (SDCNS). Specific measurements like bicaudate ratio and T2 lesion volume help distinguish these conditions.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Brain MRI findings in systemic diseases with central nervous system involvement (SDCNS) can mimic those of multiple sclerosis (MS).
- Accurate differentiation is crucial for appropriate patient management and treatment strategies.
Purpose of the Study:
- To assess and compare brain volume parameters using two-dimensional linear measurements (2DLMs) in patients with MS and SDCNS.
- To identify MRI characteristics that can help distinguish between MS and SDCNS.
Main Methods:
- Utilized two-dimensional linear measurements (2DLMs), including bicaudate ratio (BCR), corpus callosum index (CCI), and width of the third ventricle (W3V).
- Analyzed brain MRI scans from 58 MS patients and 41 SDCNS patients.
- Correlated 2DLMs with age and lesion volumes (T1LV, T2LV).
Main Results:
- In SDCNS patients, age significantly affected all 2DLMs (CCI, BCR, W3V).
- In MS patients, age affected BCR and W3V, while BCR and W3V correlated with T1LV and T2LV.
- BCR was significantly higher in SDCNS, and CCI was significantly lower in MS patients.
- A predictive model using gender, BCR, and T2LV achieved high accuracy (AUC 0.86) in distinguishing MS from SDCNS.
Conclusions:
- 2DLMs reveal distinct patterns of local brain atrophy in MS and SDCNS.
- These measurements offer valuable insights for differentiating MS from SDCNS on brain MRI.
- The study highlights the utility of 2DLMs in characterizing neurological conditions with overlapping imaging features.

