Autor: |
J, Patel, A, Pires, A, Derman, G, Fatterpekar, R E, Charlson, C, Oh, I, Kister |
Rok vydání: |
2022 |
Předmět: |
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Zdroj: |
Journal of Clinical Neuroscience. 101:32-36 |
ISSN: |
0967-5868 |
DOI: |
10.1016/j.jocn.2022.04.035 |
Popis: |
There is an unmet need to develop practical methods for differentiating multiple sclerosis (MS) from other neuroinflammatory disorders using standard brain MRI. To develop a practical approach for differentiating MS from neuromyelitis optica spectrum disorder (NMOSD) and MOG antibody-associated disorder (MOGAD) with brain MRI, we first identified lesion locations in the brain that are suggestive of MS-associated demyelination ("MS Lesion Checklist") and compared frequencies of brain lesions in the "MS Lesion Checklist" locations in a development sample of patients (n = 82) with clinically definite MS, NMOSD, and MOGAD. Patients with MS were more likely than patients with non-MS to have lesions in 3 locations only: anterior temporal horn (p 0.0001), periventricular ("Dawson's finger") (p 0.0001), and cerebellar hemisphere (p = 0.02). These three lesion locations were used as predictor variables in a multivariable regression model for discriminating MS from non-MS. The model had area under the curve (AUC) of 0.853 (95% confidence interval: 0.76-0.945), sensitivity of 87.1%, and specificity of 72.5%. We then used an independent validation sample with equal representation of MS and NMOSD/MOGAD cases (n = 97) to validate our prediction model. In the validation sample, the model was 76.3% accurate in discriminating MS from non-MS. Our simple method for predicting MS versus NMOSD/MOGAD only requires a neuroradiologist or clinician to ascertain the presence of lesions in three locations on conventional MRI sequences. It can therefore be readily applied in the real-world setting for training and clinical practice. |
Databáze: |
OpenAIRE |
Externí odkaz: |
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