Central veins in brain lesions visualized with high-field magnetic resonance imaging: a pathologically specific...

There is no single test that is diagnostic for multiple sclerosis (MS), and existing diagnostic criteria are imperfect. This can lead to diagnostic delay. Some patients require multiple (sometimes invasive) investigations, and extensive clinical follow-up to confirm or exclude a diagnosis of MS. A diagnostic biomarker that is pathologically specific for the inflammatory demyelination in MS could overhaul current diagnostic algorithms.

To prospectively assess the diagnostic value of visualizing central veins in brain lesions with magnetic resonance imaging (MRI) for patients with possible MS for whom the diagnosis is uncertain.

Prospective longitudinal cohort study. The reference standard is a clinical diagnosis that is arrived at (after a mean follow-up of 26 months) by the treating neurologist with a specialist interest in MS. The 7-T MRI scans were analyzed at baseline, by physicians blinded to the clinical data, for the presence of visible central veins.

Academic MS referral center.

A consecutive sample of 29 patients referred with possible MS who had brain lesions detected on clinical MRI scans but whose condition remained undiagnosed despite expert clinical and radiological assessments.

Seven-Tesla MRI using a T2*-weighted sequence.

The proportion of patients whose condition was correctly diagnosed as MS or as not MS, using 7-T MRI at study onset, compared with the eventual diagnosis reached by treating physicians blinded to the result of the MRI scan.

Of the 29 patients enrolled and scanned using 7-T MRI, so far 22 have received a clinical diagnosis. All 13 patients whose condition was eventually diagnosed as MS had central veins visible in the majority of brain lesions at baseline. All 9 patients whose condition was eventually not diagnosed as MS had central veins visible in a minority of lesions.

In our study, T2*-weighted 7-T MRI had 100% positive and negative predictive value for the diagnosis of MS. Clinical application of this technique could improve existing diagnostic algorithms.