Development and validation of a simple and practical method for differentiating MS from other neuroinflammatory disorders based on lesion distribution on brain MRI. (July 2022)

• Record Type: Journal Article
• Authors: Patel, J.; Pires, A.; Derman, A.; Fatterpekar, G.; Charlson, R.E.; Oh, C.; Kister, I.
• Is Part Of: Journal of clinical neuroscience. Volume 101(2022)
• Page Start: 32
• Page End: 36
• Publication Date: 2022-07
• Subjects: Multiple sclerosis -- Neuromyelitis optica spectrum disorder -- MOG antibody-associated disorder -- Brain MRI -- Diagnosis; Brain -- Surgery -- Periodicals; Neurosciences -- Periodicals; Nervous system -- Surgery -- Periodicals; Brain -- surgery -- Periodicals; Neurosurgical Procedures -- Periodicals; Neurosciences -- Periodicals; Electronic journals; 616.8
• DOI: 10.1016/j.jocn.2022.04.035
• Languages: English
• ISSNs: 0967-5868

Abstract

Highlights: MS, NMOSD, and MOGAD are different diseases that may look similar on brain MRI. We identified 3 lesion locations on brain MRI that differentiate MS from NMOSD/MOGAD. 3 locations were: "Dawson's finger, " anterior temporal horn, cerebellar hemisphere. Our predictive model was 76% accurate in discriminating MS from non-MS. Our findings are easy to apply in practice for differentiating MS from NMOSD/MOGAD. Abstract: 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.