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Original Investigation
August 19, 2019

Evaluation of the Central Vein Sign as a Diagnostic Imaging Biomarker in Multiple Sclerosis

Author Affiliations
  • 1Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital, University of Basel, Basel, Switzerland
  • 2Medical Image Analysis Center, Basel, Switzerland
  • 3Neurocure Clinical Research Center, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
  • 4qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
  • 5School of Psychology, University of Nottingham, Nottingham, United Kingdom
  • 6Clinical Neurology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
  • 7Division of Neuroradiology, Vascular and Interventional Radiology, Departments of Neurology and Radiology, Medical University of Graz, Graz, Austria
  • 8Neurology Unit, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
  • 9Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
  • 10Laboratory of Image and Data Analysis, Ilixa Ltd, London, United Kingdom
  • 11Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
  • 12Experimental and Clinical Research Center, Charité—Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health and Max Delbrück Center for Molecular Medicine, Berlin, Germany
  • 13Department of Neurology and Cerebrovascular Disorders, Poznan University of Medical Sciences, Poznan, Poland
  • 14Department of Neurology, Medical University of Graz, Graz, Austria
  • 15Section of Neuroradiology, Department of Radiology (IDI), VHIR, Barcelona, Spain
  • 16Division of Neurology, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
  • 17Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
JAMA Neurol. Published online August 19, 2019. doi:10.1001/jamaneurol.2019.2478
Key Points

Question  Is the central vein sign on clinical 3T magnetic resonance imaging a useful biomarker for the diagnosis of multiple sclerosis?

Findings  In this multicenter cross-sectional study of 4447 lesions in 606 participants, use of a 35% central vein sign proportion threshold yielded a sensitivity of 68.1% and a specificity of 82.9% for distinguishing multiple sclerosis from non–multiple sclerosis. The criteria of 3 or more central vein sign lesions had a sensitivity of 61.9% and a specificity of 89.0%.

Meaning  The 3T central vein sign–based criteria showed a high specificity in the differentiation between multiple sclerosis and non–multiple sclerosis; future studies may be needed to confirm the applicability of this finding to support the diagnosis of multiple sclerosis in clinical practice.

Abstract

Importance  The central vein sign has been proposed as a specific imaging biomarker for distinguishing between multiple sclerosis (MS) and not MS, mainly based on findings from ultrahigh-field magnetic resonance imaging (MRI) studies. The diagnostic value of the central vein sign in a multicenter setting with a variety of clinical 3 tesla (T) MRI protocols, however, remains unknown.

Objective  To evaluate the sensitivity and specificity of various central vein sign lesion criteria for differentiating MS from non-MS conditions using 3T brain MRI with various commonly used pulse sequences.

Design, Setting, and Participants  This large multicenter, cross-sectional study enrolled participants (n = 648) of ongoing observational studies and patients included in neuroimaging research databases of 8 neuroimaging centers in Europe. Patient enrollment and MRI data collection were performed between January 1, 2010, and November 30, 2016. Data analysis was conducted between January 1, 2016, and April 30, 2018. Investigators were blinded to participant diagnosis by a novel blinding procedure.

Main Outcomes and Measures  Occurrence of central vein sign was detected on 3T T2*-weighted or susceptibility-weighted imaging. Sensitivity and specificity were assessed for these MRI sequences and for different central vein sign lesion criteria, which were defined by the proportion of lesions with central vein sign or by absolute numbers of lesions with central vein sign.

Results  A total of 606 participants were included in the study after exclusion of 42 participants. Among the 606 participants, 413 (68.2%) were women. Patients with clinically isolated syndrome and relapsing-remitting MS (RRMS) included 235 women (66.6%) and had a median (range) age of 37 (14.7-61.4) years, a median (range) disease duration of 2 (0-33) years, and a median (range) Expanded Disability Status Scale score of 1.5 (0-6.5). Patients without MS included 178 women (70.4%) and had a median (range) age of 54 (18-83) years. A total of 4447 lesions were analyzed in a total of 487 patients: 690 lesions in 98 participants with clinically isolated syndrome, 2815 lesions in 225 participants with RRMS, 54 lesions in 13 participants with neuromyelitis optica spectrum disorder, 54 lesions in 14 participants with systemic lupus erythematosus, 121 lesions in 29 participants with migraine or cluster headache, 240 lesions in 20 participants with diabetes, and 473 lesions in 88 participants with other types of small-vessel disease. The sensitivity was 68.1% and specificity was 82.9% for distinguishing MS from not MS using a 35% central vein sign proportion threshold. The 3 central vein sign lesion criteria had a sensitivity of 61.9% and specificity of 89.0%. Sensitivity was higher when an optimized T2*-weighted sequence was used.

Conclusions and Relevance  In this study, use of the central vein sign at 3T MRI yielded a high specificity and a moderate sensitivity in differentiating MS from not MS; international, multicenter studies may be needed to ascertain whether the central vein sign–based criteria can accurately detect MS.

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