Brainstem atrophy in multiple sclerosis correlates with disability
Author(s): ,
L. Sander
Affiliations:
Neurologie, Universitätsspital Basel
,
S. Pezold
Affiliations:
Center for Medical Image Analysis & Navigation (CIAN)
,
S. Andermatt
Affiliations:
Center for Medical Image Analysis & Navigation (CIAN)
,
M. Amann
Affiliations:
Medical Image Analysis Center (MIAC AG) and qbig, University Basel, Basel, Switzerland
,
M.J. Wendebourg
Affiliations:
Neurologie, Universitätsspital Basel
,
T. Sinnecker
Affiliations:
Neurologie, Universitätsspital Basel
,
Y. Naegelin
Affiliations:
Neurologie, Universitätsspital Basel
,
C. Granziera
Affiliations:
Neurologie, Universitätsspital Basel
,
L. Kappos
Affiliations:
Neurologie, Universitätsspital Basel
,
J. Wuerfel
Affiliations:
Medical Image Analysis Center (MIAC AG) and qbig, University Basel, Basel, Switzerland
,
P. Cattin
Affiliations:
Center for Medical Image Analysis & Navigation (CIAN)
R. Schlaeger
Affiliations:
Neurologie, Universitätsspital Basel
ECTRIMS Online Library. Sander L. Oct 12, 2018; 228951; P1111
Laura Sander
Laura Sander
Contributions
Abstract

Abstract: P1111

Type: Poster Sessions

Abstract Category: Pathology and pathogenesis of MS - MRI and PET

Introduction: An unsolved question in multiple sclerosis (MS) research is how to protect against neurodegeneration, the major cause of progressive disability in later stages of the disease. Atrophy is one of the hallmarks of neurodegeneration in MS that can be assessed by MRI. Brainstem (BS) involvement in MS is regarded as a bad prognostic sign and can, in contrast to most other brain regions affected by MS pathology, limit life expectancy. BS atrophy is under-investigated in MS.
Objectives and Aims:
1) To investigate BS volumes on high-resolution brain 3D T1-weighted images in patients with MS compared to age- and sex matched healthy controls (HC) using a fully-automated deep learning-based segmentation approach.
2) To assess associations between BS volumes and MS disability.
Methods: A novel, fully-automated deep learning-based segmentation approach (Andermatt et al., 2016 (DOI 10.1007/978-3-319-46976-8_15); Abstract: Sander L et al.: Rapid and reliable, fully-automated brainstem segmentation for application in Multiple Sclerosis) was used to assess BS atrophy in 189 patients with diagnosis of MS or CIS according to McDonald criteria 2001 (mean age: 43.5 years, SD 11.0, 133 women, median EDSS 3.0, IQR 2, range 0-7.5, mean disease duration 12.4 years, SD 8.9) and 34 age- and sex-matched HC (mean age 43.5 years, SD 12.1, 23 women). Investigations were performed as part of an ongoing MS cohort-study and included determination of the Expanded Disability Status Score (EDSS).
Results: Compared to HC, patients showed significant reductions in whole BS volumes (% difference: 7.2%, p=0.0015), midbrain (% difference: 6.6%, p=0.0007), pons (% difference: 8.2%, p=0.0018) and medulla oblongata volumes (% difference: 4.7%, p=0.0315). Differences in volumes of the BS and BS substructures were significant in both relapsing and progressive MS subgroups. In multivariable regression analysis co-varying for age, sex and disease course, BS volume was significantly associated with EDSS (adj R2=0.33).
Conclusions: In this study, a novel, fully automated, MD-GRU based segmentation method allowed for efficient volumetry of the BS and its substructures in 200sec/scan on a Nvidia GeForce GTX 1080 GPU.MS patients show significant atrophy of the BS and its substructures compared to age- and sex matched HCs. BS volumes correlate with disability in MS. Longitudinal investigations are necessary to understand the temporal evolution and prognostic value of BS atrophy in MS.
Disclosure: Sander L, Pezold S, Andermatt S, Amann M, Wendebourg MJ: nothing to disclose. Sinnecker T has received travel support from Actelion and Roche, and speaker fees from Biogen. Naegelin Y, Granziera C, Kappos L: nothing to disclose. Wuerfel J: CEO of MIAC AG Basel, Switzerland. He served on scientific advisory boards of Actelion, Biogen, Genzyme-Sanofi, Novartis, and Roche. He is or was supported by grants of the EU (Horizon2020), German Federal Ministeries of Education and Research (BMBF) and of Economic Affairs and Energy (BMWI). Cattin P, Schlaeger R: nothing to disclose.

By clicking “Accept Terms & all Cookies” or by continuing to browse, you agree to the storing of third-party cookies on your device to enhance your user experience and agree to the user terms and conditions of this learning management system (LMS).

Cookie Settings
Accept Terms & all Cookies