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PT  - JOURNAL ARTICLE
AU  - Manning, Abigail R.
AU  - Letchuman, Vijay
AU  - Martin, Melissa L.
AU  - Gombos, Elaina
AU  - Roberts-Fitzgerald, Timothy
AU  - Cao, Quy
AU  - Raza, Praneeta
AU  - O’Donnell, Carly M.
AU  - Renner, Brian
AU  - Daboul, Lynn
AU  - Rodrigues, Paulo
AU  - Ramos, Marc
AU  - Derbyshire, John
AU  - Azevedo, Christina
AU  - Bar-Or, Amit
AU  - Caverzasi, Eduardo
AU  - Calabresi, Peter A.
AU  - Cree, Bruce A.
AU  - Freeman, Leorah
AU  - Henry, Roland G.
AU  - Longbrake, Erin E.
AU  - Oh, Jiwon
AU  - Papinutto, Nico
AU  - Pelletier, Daniel
AU  - Samudralwar, Rohini D.
AU  - Suthiphosuwan, Suradech
AU  - Schindler, Matthew K.
AU  - Bilello, Michel
AU  - Song, Jae W.
AU  - Sotirchos, Elias S.
AU  - Sicotte, Nancy L.
AU  - Al-Louzi, Omar
AU  - Solomon, Andrew J.
AU  - Reich, Daniel S.
AU  - Ontaneda, Daniel
AU  - Sati, Pascal
AU  - Shinohara, Russell T.
AU - the NAIMS Cooperative
TI  - Multicenter automated central vein sign detection performs as well as manual assessment for the diagnosis of multiple sclerosis
AID  - 10.3174/ajnr.A8510
DP  - 2024 Sep 27
TA  - American Journal of Neuroradiology
PG  - ajnr.A8510
4099  - http://www.ajnr.org/content/early/2024/09/27/ajnr.A8510.short
4100  - http://www.ajnr.org/content/early/2024/09/27/ajnr.A8510.full
AB  - BACKGROUND AND PURPOSE: The central vein sign (CVS) is a proposed diagnostic imaging biomarker for multiple sclerosis (MS). The proportion of white matter lesions exhibiting the CVS (CVS+) is higher in patients with MS compared to its radiological mimics. Evaluation for CVS+ lesions in prior studies have been performed by manual rating, an approach that is time-consuming and has variable inter-rater reliability. Accurate automated methods would facilitate efficient assessment for CVS. The objective of this study was to compare the performance of an automated CVS detection method with manual rating for the diagnosis of MS.MATERIALS AND METHODS: 3T MRI was acquired in 86 participants undergoing evaluation for MS in a 9-site multicenter study. Participants presented with either typical or atypical clinical syndromes for MS. An automated CVS detection method was employed and compared to manual rating, including total CVS+ proportion and a simplified counting method in which experts visually identified up to 6 CVS+ lesions using FLAIR* contrast (a voxel-wise product of T2 FLAIR and post-contrast T2*-EPI images).RESULTS: Automated CVS processing was completed in 79 of 86 participants (91%), of whom 28 (35%) fulfilled the 2017 McDonald criteria at the time of imaging. The area under the receiver-operator characteristic curve (AUC) for discrimination between participants with and without MS for the automated CVS approach was 0.78 (95% confidence interval: [0.67,0.88]). This was not significantly different from simplified manual counting methods (select6*) (0.80 [0.69,0.91]) or manual assessment of total CVS+ proportion (0.89 [0.82,0.96]). In a sensitivity analysis excluding 11 participants whose MRI exhibited motion artifact, the AUC for the automated method was 0.81 [0.70,0.91], which was not statistically different from that for select6* (0.79 [0.68,0.92]) or manual assessment of total CVS+ proportion (0.89 [0.81,0.97]).CONCLUSIONS: Automated CVS assessment was comparable to manual CVS scoring for differentiating patients with MS from those with other diagnoses. Large, prospective, multicenter studies utilizing automated methods and enrolling the breadth of disorders referred for suspicion of MS are needed to determine optimal approaches for clinical implementation of an automated CVS detection method.ABBREVIATIONS: CVS= central vein sign; CVS+ = white matter lesions exhibiting the CVS; MRI = magnetic resonance imaging; MS = multiple sclerosis; T2 FLAIR = T2 fluid-attenuated inversion recovery; T2*-EPI = T2*-weighted 3D echo planar imaging; FLAIR* = a voxel-wise product of T2 FLAIR and post-contrast T2*-EPI images; select6* = simplified counting method in which experts visually identified up to 6 CVS+ lesions on FLAIR* imaging.