King's College London

Research portal

Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral

Research output: Contribution to journalArticle

Standard

Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral. / for the MAGNIMS Study Group.

In: Annals of Neurology, Vol. 86, No. 5, 01.11.2019, p. 704-713.

Research output: Contribution to journalArticle

Harvard

for the MAGNIMS Study Group 2019, 'Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral', Annals of Neurology, vol. 86, no. 5, pp. 704-713. https://doi.org/10.1002/ana.25571

APA

for the MAGNIMS Study Group (2019). Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral. Annals of Neurology, 86(5), 704-713. https://doi.org/10.1002/ana.25571

Vancouver

for the MAGNIMS Study Group. Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral. Annals of Neurology. 2019 Nov 1;86(5):704-713. https://doi.org/10.1002/ana.25571

Author

for the MAGNIMS Study Group. / Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral. In: Annals of Neurology. 2019 ; Vol. 86, No. 5. pp. 704-713.

Bibtex Download

@article{372fdfb597514a338ab5c8495e3c6026,
title = "Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral",
abstract = "Objective: Spinal cord atrophy is a clinically relevant feature of multiple sclerosis (MS), but longitudinal assessments on magnetic resonance imaging using segmentation-based methods suffer from measurement variability, especially in multicenter studies. We compared the generalized boundary shift integral (GBSI), a registration-based method, with a standard segmentation-based method. Methods: Baseline and 1-year spinal cord 3-dimensional T1-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 8 MAGNIMS (Magnetic Resonance Imaging in Multiple Sclerosis) sites on multimanufacturer 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 percentage annual volume change was calculated. Results: GBSI provided similar rates of atrophy, but reduced measurement variability compared to 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 (area under the curve [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 vs 0.50; p = 0.04). Sample size to detect 60{\%} treatment effect on spinal cord atrophy over 1 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 a conventional segmentation-based method (CSA), although it is still far from perfect. ANN NEUROL 2019.",
author = "{for the MAGNIMS Study Group} and Marcello Moccia and Ferran Prados and Massimo Filippi and Rocca, {Maria A.} and Paola Valsasina and Brownlee, {Wallace J.} and Chiara Zecca and Antonio Gallo and Alex Rovira and Achim Gass and Jacqueline Palace and Carsten Lukas and Hugo Vrenken and Sebastien Ourselin and {Gandini Wheeler-Kingshott}, {Claudia A.M.} and Olga Ciccarelli and Frederik Barkhof",
year = "2019",
month = "11",
day = "1",
doi = "10.1002/ana.25571",
language = "English",
volume = "86",
pages = "704--713",
journal = "Annals of Neurology",
issn = "0364-5134",
publisher = "John Wiley and Sons Inc.",
number = "5",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral

AU - for the MAGNIMS Study Group

AU - Moccia, Marcello

AU - Prados, Ferran

AU - Filippi, Massimo

AU - Rocca, Maria A.

AU - Valsasina, Paola

AU - Brownlee, Wallace J.

AU - Zecca, Chiara

AU - Gallo, Antonio

AU - Rovira, Alex

AU - Gass, Achim

AU - Palace, Jacqueline

AU - Lukas, Carsten

AU - Vrenken, Hugo

AU - Ourselin, Sebastien

AU - Gandini Wheeler-Kingshott, Claudia A.M.

AU - Ciccarelli, Olga

AU - Barkhof, Frederik

PY - 2019/11/1

Y1 - 2019/11/1

N2 - Objective: Spinal cord atrophy is a clinically relevant feature of multiple sclerosis (MS), but longitudinal assessments on magnetic resonance imaging using segmentation-based methods suffer from measurement variability, especially in multicenter studies. We compared the generalized boundary shift integral (GBSI), a registration-based method, with a standard segmentation-based method. Methods: Baseline and 1-year spinal cord 3-dimensional T1-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 8 MAGNIMS (Magnetic Resonance Imaging in Multiple Sclerosis) sites on multimanufacturer 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 percentage annual volume change was calculated. Results: GBSI provided similar rates of atrophy, but reduced measurement variability compared to 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 (area under the curve [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 vs 0.50; p = 0.04). Sample size to detect 60% treatment effect on spinal cord atrophy over 1 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 a conventional segmentation-based method (CSA), although it is still far from perfect. ANN NEUROL 2019.

AB - Objective: Spinal cord atrophy is a clinically relevant feature of multiple sclerosis (MS), but longitudinal assessments on magnetic resonance imaging using segmentation-based methods suffer from measurement variability, especially in multicenter studies. We compared the generalized boundary shift integral (GBSI), a registration-based method, with a standard segmentation-based method. Methods: Baseline and 1-year spinal cord 3-dimensional T1-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 8 MAGNIMS (Magnetic Resonance Imaging in Multiple Sclerosis) sites on multimanufacturer 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 percentage annual volume change was calculated. Results: GBSI provided similar rates of atrophy, but reduced measurement variability compared to 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 (area under the curve [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 vs 0.50; p = 0.04). Sample size to detect 60% treatment effect on spinal cord atrophy over 1 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 a conventional segmentation-based method (CSA), although it is still far from perfect. ANN NEUROL 2019.

UR - http://www.scopus.com/inward/record.url?scp=85070923988&partnerID=8YFLogxK

U2 - 10.1002/ana.25571

DO - 10.1002/ana.25571

M3 - Article

AN - SCOPUS:85070923988

VL - 86

SP - 704

EP - 713

JO - Annals of Neurology

JF - Annals of Neurology

SN - 0364-5134

IS - 5

ER -

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454