Literature DB >> 9397021

Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis.

F Barkhof1, M Filippi, D H Miller, P Scheltens, A Campi, C H Polman, G Comi, H J Adèr, N Losseff, J Valk.   

Abstract

We compared MRI criteria used to predict conversion of suspected multiple sclerosis to clinically definite multiple sclerosis. Seventy-four patients with clinically isolated neurological symptoms suggestive of multiple sclerosis were studied with MRI. Logistic regression analysis was used to remove redundant information, and a diagnostic model was built after each MRI parameter was dichotomized according to maximum accuracy using receiver operating characteristic analysis. Clinically definite multiple sclerosis developed in 33 patients (prevalence 45%). The optimum cut-off point (number of lesions) was one for most MRI criteria (including gadolinium-enhancement and juxta-cortical lesions), but three for periventricular lesions, and nine for the total number of T2-lesions. Only gadolinium-enhancement and juxta-cortical lesions provided independent information. A final model which, in addition, included infratentorial and periventricular lesions, had an accuracy of 80%, and having more abnormal criteria, predicted conversion to clinically definite multiple sclerosis strongly. The model performed better than the criteria of Paty et al. (Neurology 1988; 38: 180-5) and of Fazekas et al. (Neurology 1988; 38: 1822-5). We concluded that a four-parameter dichotomized MRI model including gadolinium-enhancement, juxtacortical, infratentorial and periventricular lesions best predicts conversion to clinically definite multiple sclerosis.

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Year:  1997        PMID: 9397021     DOI: 10.1093/brain/120.11.2059

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   13.501


  243 in total

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Review 9.  The radiologically isolated syndrome: take action when the unexpected is uncovered?

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10.  Pathologic heterogeneity persists in early active multiple sclerosis lesions.

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