MRI-Based Manual versus Automated Corpus Callosum Volumetric Measurements in Multiple Sclerosis.
MRI
Multiple sclerosis
atrophy
corpus callosum
Journal
Journal of neuroimaging : official journal of the American Society of Neuroimaging
ISSN: 1552-6569
Titre abrégé: J Neuroimaging
Pays: United States
ID NLM: 9102705
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
15
07
2019
revised:
13
10
2019
accepted:
26
10
2019
pubmed:
22
11
2019
medline:
15
12
2020
entrez:
22
11
2019
Statut:
ppublish
Résumé
Corpus callosum atrophy is a neurodegenerative biomarker in multiple sclerosis (MS). Manual delineations are gold standard but subjective and labor intensive. Novel automated methods are promising but require validation. We aimed to compare the robustness of manual versus automatic corpus callosum segmentations based on FreeSurfer. Nine MS patients (6 females, age 38 ± 13 years, disease duration 7.3 ± 5.2 years) were scanned twice with repositioning using 3-dimensional T Manual measurements had high intrarater (junior doctor .96 and neuroradiologist .96) and interrater agreement (.94), by intraclass correlation coefficient (P < .001). The coefficient of variation was lowest for longitudinal FreeSurfer (.96% within scanners; 2.0% between scanners) compared to cross-sectional FreeSurfer (3.7%, P = .001; 3.8%, P = .058) and the neuroradiologist (2.3%, P = .005; 2.4%, P = .33). Longitudinal FreeSurfer was also more accurate than cross-sectional (Dice scores 83.9 ± 7.5% vs. 78.9 ± 8.4%, P < .01 relative to manual segmentations). The corpus callosum measures correlated with physical disability (longitudinal FreeSurfer r = -.36, P < .01; neuroradiologist r = -.32, P < .01) and cognitive disability (longitudinal FreeSurfer r = .68, P < .001; neuroradiologist r = .64, P < .001). FreeSurfer's longitudinal stream provides corpus callosum measures with better repeatability than current manual methods and with similar clinical correlations. However, due to some limitations in accuracy, caution is warranted when using FreeSurfer with clinical data.
Sections du résumé
BACKGROUND AND PURPOSE
Corpus callosum atrophy is a neurodegenerative biomarker in multiple sclerosis (MS). Manual delineations are gold standard but subjective and labor intensive. Novel automated methods are promising but require validation. We aimed to compare the robustness of manual versus automatic corpus callosum segmentations based on FreeSurfer.
METHODS
Nine MS patients (6 females, age 38 ± 13 years, disease duration 7.3 ± 5.2 years) were scanned twice with repositioning using 3-dimensional T
RESULTS
Manual measurements had high intrarater (junior doctor .96 and neuroradiologist .96) and interrater agreement (.94), by intraclass correlation coefficient (P < .001). The coefficient of variation was lowest for longitudinal FreeSurfer (.96% within scanners; 2.0% between scanners) compared to cross-sectional FreeSurfer (3.7%, P = .001; 3.8%, P = .058) and the neuroradiologist (2.3%, P = .005; 2.4%, P = .33). Longitudinal FreeSurfer was also more accurate than cross-sectional (Dice scores 83.9 ± 7.5% vs. 78.9 ± 8.4%, P < .01 relative to manual segmentations). The corpus callosum measures correlated with physical disability (longitudinal FreeSurfer r = -.36, P < .01; neuroradiologist r = -.32, P < .01) and cognitive disability (longitudinal FreeSurfer r = .68, P < .001; neuroradiologist r = .64, P < .001).
CONCLUSIONS
FreeSurfer's longitudinal stream provides corpus callosum measures with better repeatability than current manual methods and with similar clinical correlations. However, due to some limitations in accuracy, caution is warranted when using FreeSurfer with clinical data.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
198-204Informations de copyright
© 2019 by the American Society of Neuroimaging.
Références
Koch-Henriksen N, Sørensen PS. The changing demographic pattern of multiple sclerosis epidemiology. Lancet Neurol 2010;9:520-32.
GBD 2015 Neurological Disorders Collaborator Group. Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol 2017;16:877-97.
Gyllensten H, Wiberg M, Alexanderson K, et al. Costs of illness of multiple sclerosis in Sweden: a population-based register study of people of working age. Eur J Health Econ 2018;19:435-46.
Ciotti JR, Cross AH. Disease-modifying treatment in progressive multiple sclerosis. Curr Treat Options Neurol 2018;20:12.
Feinstein A, Freeman J, Lo AC. Treatment of progressive multiple sclerosis: what works, what does not, and what is needed. Lancet Neurol 2015;14:194-207.
Dekker I, Eijlers AJC, Popescu V, et al. Predicting clinical progression in multiple sclerosis after six and twelve years. Eur J Neurol 2019;26:893-902.
Evangelou N, Konz D, Esiri MM, et al. Regional axonal loss in the corpus callosum correlates with cerebral white matter lesion volume and distribution in multiple sclerosis. Brain 2000;123:1845-9.
Mitchell TN, Free SL, Merschhemke M, et al. Reliable callosal measurement: population normative data confirm sex-related differences. AJNR Am J Neuroradiol 2003;24:410-8.
Martola J, Stawiarz L, Fredrikson S, et al. Progression of non-age-related callosal brain atrophy in multiple sclerosis: a 9-year longitudinal MRI study representing four decades of disease development. J Neurol Neurosurg Psychiatry 2007;78:375-80.
Granberg T, Bergendal G, Shams S, et al. MRI-defined corpus callosal atrophy in multiple sclerosis: a comparison of volumetric measurements, corpus callosum area and index. J Neuroimaging 2015;25:996-1001.
Guo C, Ferreira D, Fink K, et al. Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis. Eur Radiol 2018;29:1355-64.
Reuter M, Schmansky NJ, Rosas HD, et al. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 2012;61:1402-18.
Polman CH, Reingold SC, Banwell B, et al. Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria. Ann Neurol 2011;69:292-302.
Kurtzke J. Rating neurologic impairment in multiple sclerosis an expanded disability status scale (EDSS). Neurology 1983;33:1444-52.
Lezak MD, Howieson DB, Bigler ED, Tranel D. Neuropsychological Assessment. Oxford: Oxford University Press, 2012.
Granberg T, Martola J, Bergendal G, et al. Corpus callosum atrophy is strongly associated with cognitive impairment in multiple sclerosis: results of a 17-year longitudinal study. Mult Scler J 2015;21:1151-8.
Reuter M, Rosas HD, Fischl B. Highly accurate inverse consistent registration: A robust approach. Neuroimage 2010;53:1181-96.
Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016;15:155-63.
Dill V, Franco AR, Pinho MS. Automated methods for hippocampus segmentation: the evolution and a review of the state of the art. Neuroinformatics 2015;13:133-50.
Van Schependom J, Jain S, Cambron M, et al. Reliability of measuring regional callosal atrophy in neurodegenerative diseases. Neuroimage Clin 2016;12:825-31.
Klawiter EC, Ceccarelli A, Arora A, et al. Corpus callosum atrophy correlates with gray matter atrophy in patients with multiple sclerosis. J Neuroimaging 2015;25:62-7.
Gifuni AJ, Olié E, Ding Y, et al. Corpus callosum volumes in bipolar disorders and suicidal vulnerability. Psychiatry Res Neuroimaging 2017;262:47-54.
Goldman JG, Bledsoe IO, Merkitch D, et al. Corpus callosal atrophy and associations with cognitive impairment in Parkinson disease. Neurology 2017;88:1265-72.
Ouellette R, Bergendal Å, Shams S, et al. Lesion accumulation is predictive of long-term cognitive decline in multiple sclerosis. Mult Scler Relat Disord 2018;21:110-6.
Guenette JP, Stern RA, Tripodis Y, et al. Automated versus manual segmentation of brain region volumes in former football players. Neuroimage Clin 2018;18:888-96.
Heinen R, Bouvy WH, Mendrik AM, et al. Robustness of automated methods for brain volume measurements across different MRI field strengths. PLoS One 2016;11:e0165719.
Vågberg M, Axelsson M, Birgander R, et al. Guidelines for the use of magnetic resonance imaging in diagnosing and monitoring the treatment of multiple sclerosis: recommendations of the Swedish Multiple Sclerosis Association and the Swedish Neuroradiological Society. Acta Neurol Scand 2017;135:17-24.
Klasson N, Olsson E, Eckerström C, et al. Estimated intracranial volume from FreeSurfer is biased by total brain volume. Eur Radiol Exp 2018;2:24.
Yaldizli Ö, Penner I-K, Frontzek K, et al. The relationship between total and regional corpus callosum atrophy, cognitive impairment and fatigue in multiple sclerosis patients. Mult Scler 2014;20:356-64.
Bergendal G, Martola J, Stawiarz L, et al. Callosal atrophy in multiple sclerosis is related to cognitive speed. Acta Neurol Scand 2013;127:281-9.
Bodini B, Cercignani M, Khaleeli Z, et al. Corpus callosum damage predicts disability progression and cognitive dysfunction in primary-progressive MS after five years. Hum Brain Mapp 2013;34:1163-72.
Sampat MP, Healy BC, Meier DS, et al. Disease modeling in multiple sclerosis: assessment and quantification of sources of variability in brain parenchymal fraction measurements. Neuroimage 2010;52:1367-73.
Sormani MP, Arnold DL, De Stefano N. Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis. Ann Neurol 2014;75:43-9.