Lesion size and shape in central vein sign assessment for multiple sclerosis diagnosis: An in vivo and postmortem MRI study
Year: 2022
Type: article
Study design: Observational Study
Abstract: BACKGROUND: The "central vein sign" (CVS), a linear hypointensity on T2*-weighted imaging corresponding to a central vein/venule, is associated with multiple sclerosis (MS) lesions. The effect of lesion-size exclusion criteria on MS diagnostic accuracy has not been extensively studied. OBJECTIVE: Investigate the optimal lesion-size exclusion criteria for CVS use in MS diagnosis. METHODS: Cross-sectional study of 163 MS and 51 non-MS, and radiological/histopathological correlation of 5 MS and 1 control autopsy cases. The effects of lesion-size exclusion on MS diagnosis using the CVS, and intralesional vein detection on histopathology were evaluated. RESULTS: = 0.004). CVS percentage-based criteria with no lesion-size exclusion showed the highest diagnostic accuracy in differentiating MS cases. However, a simple count of three or more CVS+ lesions greater than 3.5 mm is highly accurate and can be rapidly implemented (sensitivity 93%; specificity 88%). On magnetic resonance imaging (MRI)-histopathological correlation, the CVS had high specificity for identifying intralesional veins (0/7 false positives). CONCLUSION: Lesion-size measures add important information when using CVS+ lesion counts for MS diagnosis. The CVS is a specific biomarker corresponding to intralesional veins on histopathology.
Source: Multiple Sclerosis Journal
Institutions: Cedars-Sinai Medical Center, National Institute of Neurological Disorders and Stroke, Johns Hopkins University, Vita-Salute San Raffaele University, Johns Hopkins Medicine, IRCCS Ospedale San Raffaele, IRCCS San Raffaele Roma, National Cancer Institute, Center for Cancer Research, Icahn School of Medicine at Mount Sinai, National Institute of Mental Health, Henry M. Jackson Foundation, Center for Neuroscience and Regenerative Medicine
Language: English
Subfield: Pathology and Forensic Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4282938583
