Moccia, M;
Prados Carrasco, F;
Filippi, M;
Rocca, M;
Valsasina, P;
Brownlee, W;
Zecca, C;
... for the MAGNIMS Study Group, .; + view all
(2019)
Longitudinal spinal cor atrophy in multiple sclerosis using the generalised boundary shift integral.
Annals of Neurology
, 86
(5)
pp. 704-713.
10.1002/ana.25571.
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Abstract
Objectives: Spinal cord atrophy is a clinically relevant feature of multiple sclerosis (MS), but longitudinal assessments on MRI using segmentation-based methods suffer from measurement variability, especially in multicentre studies. We compared the generalised boundary shift integral (GBSI), a registration-based method, with standard segmentation-based method. // Methods: Baseline and 1-year spinal cord 3DT1-weighted images (1mm isotropic) were obtained from 282 patients (52 clinically isolated syndrome (CIS), 196 relapsing-remitting MS (RRMS), 34 progressive MS (PMS)), and 82 controls from eight MAGNIMS sites, on multi-manufacturer and multi-field strength scans. Spinal Cord Toolbox was used for C2-5 segmentation and cross-sectional area (CSA) calculation. After cord straightening and registration, GBSI measured atrophy based on the probabilistic boundary-shift region-of-interest. CSA and GBSI percent annual volume change was calculated. // Results: GBSI provided similar rates of atrophy, but reduced measurement variability than CSA in all MS subtypes (CIS: -0.95±2.11% vs. -1.19±3.67%; RRMS: -1.74±2.57% vs. -1.74±4.02%; PMS: -2.29±2.40% vs. -1.29±3.20%), and healthy controls (0.02±2.39% vs. -0.56 ±3.77%). GBSI performed better than CSA in differentiating healthy controls from CIS (AUC=0.66 vs. 0.53; p=0.03), RRMS (AUC=0.73 vs. 0.59; p<0.001), PMS (AUC=0.77 vs. 0.53; p<0.001), and patients with disability progression from patients without progression (AUC=0.59 versus 0.50; p=0.04). Sample size to detect 60% treatment effect on spinal cord atrophy over one year was lower for GBSI than CSA (CIS: 106 vs. 830; RRMS: 95 vs. 335; PMS: 44 vs. 215) (power=80%; alpha=5%). // Interpretation: The registration-based method (GBSI) allowed better separation between MS patients and healthy controls and improved statistical power, when compared with conventional segmentation-based method (CSA), though still far from perfect.
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