FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis.


Journal

NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070

Informations de publication

Date de publication:
2021
Historique:
received: 19 11 2020
accepted: 20 12 2020
pubmed: 9 1 2021
medline: 29 6 2021
entrez: 8 1 2021
Statut: ppublish

Résumé

MRI assessment in multiple sclerosis (MS) focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor. It is not routinely reported clinically, in part due to difficulty in achieving reproducible measurements. Automated MRI quantification of WM lesions and brain volume could provide important clinical monitoring data. In general, lesion quantification relies on both T1 and FLAIR input images, while tissue volumetry relies on T1. However, T1-weighted scans are not routinely included in the clinical MS protocol, limiting the utility of automated quantification. We address an aspect of this important translational challenge by assessing the performance of FLAIR-only lesion and brain segmentation, against a conventional approach requiring multi-contrast acquisition. We explore whether FLAIR-only grey matter (GM) segmentation yields more variability in performance compared with two-channel segmentation; whether this is related to field strength; and whether the results meet a level of clinical acceptability demonstrated by the ability to reproduce established biological associations. We used a multicentre dataset of subjects with a CIS suggestive of MS scanned at 1.5T and 3T in the same week. WM lesions were manually segmented by two raters, 'manual 1' guided by consensus reading of CIS-specific lesions and 'manual 2' by any WM hyperintensity. An existing brain segmentation method was adapted for FLAIR-only input. Automated segmentation of WM hyperintensity and brain volumes were performed with conventional (T1/T1 + FLAIR) and FLAIR-only methods. WM lesion volumes were comparable at 1.5T between 'manual 2' and FLAIR-only methods and at 3T between 'manual 2', T1 + FLAIR and FLAIR-only methods. For cortical GM volume, linear regression measures between conventional and FLAIR-only segmentation were high (1.5T: α = 1.029, R FLAIR-only automated segmentation of WM lesions and brain volumes were consistent with results obtained through conventional methods and had the ability to demonstrate biological effects in our study population. Imaging protocol harmonisation and validation with other MS phenotypes could facilitate the integration of automated WM lesion volume and brain atrophy analysis as clinical tools in radiological MS reporting.

Sections du résumé

BACKGROUND
MRI assessment in multiple sclerosis (MS) focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor. It is not routinely reported clinically, in part due to difficulty in achieving reproducible measurements. Automated MRI quantification of WM lesions and brain volume could provide important clinical monitoring data. In general, lesion quantification relies on both T1 and FLAIR input images, while tissue volumetry relies on T1. However, T1-weighted scans are not routinely included in the clinical MS protocol, limiting the utility of automated quantification.
OBJECTIVES
We address an aspect of this important translational challenge by assessing the performance of FLAIR-only lesion and brain segmentation, against a conventional approach requiring multi-contrast acquisition. We explore whether FLAIR-only grey matter (GM) segmentation yields more variability in performance compared with two-channel segmentation; whether this is related to field strength; and whether the results meet a level of clinical acceptability demonstrated by the ability to reproduce established biological associations.
METHODS
We used a multicentre dataset of subjects with a CIS suggestive of MS scanned at 1.5T and 3T in the same week. WM lesions were manually segmented by two raters, 'manual 1' guided by consensus reading of CIS-specific lesions and 'manual 2' by any WM hyperintensity. An existing brain segmentation method was adapted for FLAIR-only input. Automated segmentation of WM hyperintensity and brain volumes were performed with conventional (T1/T1 + FLAIR) and FLAIR-only methods.
RESULTS
WM lesion volumes were comparable at 1.5T between 'manual 2' and FLAIR-only methods and at 3T between 'manual 2', T1 + FLAIR and FLAIR-only methods. For cortical GM volume, linear regression measures between conventional and FLAIR-only segmentation were high (1.5T: α = 1.029, R
CONCLUSIONS
FLAIR-only automated segmentation of WM lesions and brain volumes were consistent with results obtained through conventional methods and had the ability to demonstrate biological effects in our study population. Imaging protocol harmonisation and validation with other MS phenotypes could facilitate the integration of automated WM lesion volume and brain atrophy analysis as clinical tools in radiological MS reporting.

Identifiants

pubmed: 33418171
pii: S2213-1582(20)30379-X
doi: 10.1016/j.nicl.2020.102542
pmc: PMC7804983
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

102542

Subventions

Organisme : EPA
ID : EP-W-17-011
Pays : United States
Organisme : Medical Research Council
ID : MR/M009106/1
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

Auteurs

O Goodkin (O)

Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. Electronic address: o.goodkin@ucl.ac.uk.

F Prados (F)

Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; eHealth Centre, Universitat Oberta de Catalunya, Barcelona, Spain.

S B Vos (SB)

Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom.

H Pemberton (H)

Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.

S Collorone (S)

NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, United Kingdom.

M H J Hagens (MHJ)

MS Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

M J Cardoso (MJ)

School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

T A Yousry (TA)

Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom.

J S Thornton (JS)

Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom.

C H Sudre (CH)

School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.

F Barkhof (F)

Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom; Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH