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.

Keywords

Multiple sclerosisMedicineNeurologyRadiologyLogistic regressionMagnetic resonance imagingNuclear medicineInternal medicine

Affiliated Institutions

Related Publications

Publication Info

Year
1997
Type
review
Volume
120
Issue
11
Pages
2059-2069
Citations
1237
Access
Closed

External Links

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1237
OpenAlex

Cite This

Frederik Barkhof, Massimo Filippi, David H. Miller et al. (1997). Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain , 120 (11) , 2059-2069. https://doi.org/10.1093/brain/120.11.2059

Identifiers

DOI
10.1093/brain/120.11.2059