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Related Concept Videos

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Related Experiment Video

Updated: Jun 18, 2026

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions
07:30

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Published on: April 23, 2021

Callosal lesion predicts future attacks after clinically isolated syndrome.

N Jafari1, K L Kreft, H Z Flach

  • 1Department of Neurology, MS Centre ErasMS, 3000 CA Rotterdam, The Netherlands.

Neurology
|December 2, 2009
PubMed
Summary

Corpus callosum lesions on MRI, alongside Barkhof criteria, improve prediction of multiple sclerosis (MS) conversion after clinically isolated syndrome (CIS). Combining these markers offers a stronger predictive tool for MS development.

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Area of Science:

  • Neurology
  • Radiology
  • Immunology

Background:

  • Clinically isolated syndrome (CIS) is an early stage of multiple sclerosis (MS).
  • Current MRI criteria, like the Barkhof criteria, aid in predicting MS conversion.
  • Corpus callosum (CC) lesions are associated with MS and may offer additional predictive value.

Purpose of the Study:

  • To evaluate if CC lesions on MRI enhance the prediction of MS conversion in CIS patients.
  • To assess the combined predictive power of CC lesions and Barkhof criteria.

Main Methods:

  • A follow-up study of 158 CIS patients who underwent MRI.
  • MRI scans were analyzed for Barkhof criteria and the presence of CC lesions.
  • Cox regression models analyzed time to conversion to MS, diagnosed using Poser criteria.

Main Results:

  • Both Barkhof criteria (HR 2.6) and CC lesions (HR 2.7) were significantly associated with MS conversion.
  • The combined prediction of Barkhof criteria and CC lesions yielded a higher hazard ratio (HR 3.3).
  • Patients without Barkhof criteria but with CC lesions showed a fourfold increased risk of MS (HR 3.8).

Conclusions:

  • CC lesions and Barkhof criteria are independent predictors of MS conversion from CIS.
  • Combining CC lesion assessment with Barkhof criteria strengthens MS prediction.
  • Evaluating CC lesions is a valuable supplementary tool for prognostication in CIS.