Multiple sclerosis versus cerebral small vessel disease in MRI: a practical approach using qualitative and
Sabahattin Yuzkan1, Serdar Balsak2, Ufuk Cinkir3
1Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.
Background:
Multiple sclerosis (MS) and cerebral small vessel disease (CSVD) are relatively common radiological entities that occasionally necessitate differential diagnosis.
Purpose:
To investigate the differences in magnetic resonance imaging (MRI) signal intensity (SI) between MS and CSVD related white matter lesions.
Material And Methods:
On 1.5-T and 3-T MRI scanners, 50 patients with MS (380 lesions) and 50 patients with CSVD (395 lesions) were retrospectively evaluated. Visual inspection was used to conduct qualitative analysis on diffusion-weighted imaging (DWI)_b1000 to determine relative signal intensity. The thalamus served as the reference for quantitative analysis based on SI ratio (SIR). The statistical analysis utilized univariable and multivariable methods. There were analyses of patient and lesion datasets. On a dataset restricted by age (30-50 years), additional evaluations, including unsupervised fuzzy c-means clustering, were performed.
Results:
Using both quantitative and qualitative features, the optimal model achieved a 100% accuracy, sensitivity, and specificity with an area under the curve (AUC) of 1 in patient-wise analysis. With an AUC of 0.984, the best model achieved a 94% accuracy, sensitivity, and specificity when using only quantitative features. The model's accuracy, sensitivity, and specificity were 91.9%, 84.6%, and 95.8%, respectively, when using the age-restricted dataset. Independent predictors were T2_SIR_max (optimal cutoff=2.1) and DWI_b1000_SIR_mean (optimal cutoff=1.1). Clustering also performed well with an accuracy, sensitivity, and specificity of 86.5%, 70.6%, and 100%, respectively, in the age-restricted dataset.
Conclusion:
SI characteristics derived from DWI_b1000 and T2-weighted-based MRI demonstrate excellent performance in differentiating white matter lesions caused by MS and CSVD.
Insights
Magnetic resonance imaging (MRI) signal intensity characteristics effectively differentiate multiple sclerosis (MS) and cerebral small vessel disease (CSVD) white matter lesions. This study highlights MRI
Area of Science:
- Radiology
- Neurology
- Medical Imaging Analysis
Background:
- Multiple sclerosis (MS) and cerebral small vessel disease (CSVD) are common conditions often requiring differential diagnosis.
- Distinguishing between MS and CSVD white matter lesions is crucial for accurate patient management.
Purpose of the Study:
- To investigate differences in magnetic resonance imaging (MRI) signal intensity (SI) between MS and CSVD white matter lesions.
- To develop and validate an MRI-based model for differentiating these two conditions.
Main Methods:
- Retrospective analysis of 50 patients with MS (380 lesions) and 50 with CSVD (395 lesions) using 1.5-T and 3-T MRI scanners.
- Qualitative and quantitative analysis of diffusion-weighted imaging (DWI)_b1000 and T2-weighted imaging, using the thalamus as a reference for signal intensity ratio (SIR).
- Statistical analysis included univariable, multivariable, and unsupervised fuzzy c-means clustering on age-restricted datasets.
Main Results:
- An optimal model achieved 100% accuracy, sensitivity, and specificity in patient-wise analysis using both quantitative and qualitative MRI features.
- A model using only quantitative features achieved 94% accuracy, sensitivity, and specificity (AUC=0.984).
- Key predictors identified were T2_SIR_max and DWI_b1000_SIR_mean; clustering also showed high performance.
Conclusions:
- Signal intensity characteristics from DWI_b1000 and T2-weighted MRI are highly effective in differentiating MS and CSVD white matter lesions.
- The findings support the use of specific MRI SI metrics for improved diagnostic accuracy in distinguishing MS from CSVD.
