Exploring individual multiple sclerosis lesion volume change over time: Development of an algorithm for the analyses of longitudinal quantitative MRI measures.
Lesion tracking
Medical image analysis
Multiple sclerosis
Quantitative magnetic resonance imaging
Therapy monitoring
Volumetric assessment
Journal
NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070
Informations de publication
Date de publication:
2019
2019
Historique:
received:
03
08
2018
revised:
01
11
2018
accepted:
01
12
2018
pubmed:
14
12
2018
medline:
18
12
2019
entrez:
15
12
2018
Statut:
ppublish
Résumé
Magnetic resonance imaging (MRI) is used to follow-up multiple sclerosis (MS) and evaluate disease progression and therapy response via lesion quantification. However, there is a lack of automated post-processing techniques to quantify individual MS lesion change. The present study developed a secondary post-processing algorithm for MS lesion segmentation routine to quantify individual changes in volume over time. An Automatic Follow-up of Individual Lesions (AFIL) algorithm was developed to process time series of pre-segmented binary lesion masks. The resulting consistently labelled lesion masks allowed for the evaluation of individual lesion volumes. Algorithm performance testing was executed in seven early MS patients with four MRI visits, and MS experienced readers verified the accuracy. AFIL distinguished 328 individual MS lesions with a 0.9% error rate to track persistent or new lesions based on expert assessment. A total of 121 new lesions evolved within the observed time period. The proportional courses of 69.1% lesions in the persistent lesion population exhibited varying volume, 16.9% exhibited stable volume, 3.4% exhibiting continuously increasing, and 0.5% exhibited continuously decreasing volume. This algorithm tracked individual lesions to automatically create an individual lesion growth profile of MS patients. This approach may allow for characterization of patients based on their individual lesion progression.
Sections du résumé
BACKGROUND
Magnetic resonance imaging (MRI) is used to follow-up multiple sclerosis (MS) and evaluate disease progression and therapy response via lesion quantification. However, there is a lack of automated post-processing techniques to quantify individual MS lesion change.
OBJECTIVE
The present study developed a secondary post-processing algorithm for MS lesion segmentation routine to quantify individual changes in volume over time.
METHODS
An Automatic Follow-up of Individual Lesions (AFIL) algorithm was developed to process time series of pre-segmented binary lesion masks. The resulting consistently labelled lesion masks allowed for the evaluation of individual lesion volumes. Algorithm performance testing was executed in seven early MS patients with four MRI visits, and MS experienced readers verified the accuracy.
RESULTS
AFIL distinguished 328 individual MS lesions with a 0.9% error rate to track persistent or new lesions based on expert assessment. A total of 121 new lesions evolved within the observed time period. The proportional courses of 69.1% lesions in the persistent lesion population exhibited varying volume, 16.9% exhibited stable volume, 3.4% exhibiting continuously increasing, and 0.5% exhibited continuously decreasing volume.
CONCLUSION
This algorithm tracked individual lesions to automatically create an individual lesion growth profile of MS patients. This approach may allow for characterization of patients based on their individual lesion progression.
Identifiants
pubmed: 30545687
pii: S2213-1582(18)30371-1
doi: 10.1016/j.nicl.2018.101623
pmc: PMC6411650
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
101623Informations de copyright
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.