Multicenter Automated Central Vein Sign Detection Performs as Well as Manual Assessment for the Diagnosis of Multiple Sclerosis

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 with its radiologic mimics. Evaluation for CVS+ lesions in prior studies has been performed by manual rating, an approach that is time-consuming and has variable interrater 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. 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 with manual rating, including total CVS+ proportion and a simplified counting method in which experts visually identified up to 6 CVS+ lesions by using FLAIR* contrast (a voxelwise product of T2 FLAIR and postcontrast T2*-EPI). 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 operating characteristic curve (AUC) for discrimination between participants with and without MS for the automated CVS approach was 0.78 (95% CI: [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]). 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.