Considerations for Mean Upper Cervical Cord Area Implementation in a Longitudinal MRI Setting: Methods, Interrater Reliability, and MRI Quality Control

Overview

Journal AJNR Am J Neuroradiol

Specialty Neurology

Date 2020 Jan 25

PMID 31974079

Citations 5

Authors

C Chien

V Juenger

M Scheel

A U Brandt

F Paul

Affiliations

Soon will be listed here.

Abstract

Background And Purpose: Spinal cord atrophy is commonly measured from cerebral MRIs, including the upper cervical cord. However, rescan intraparticipant measures have not been investigated in healthy cohorts. This study investigated technical and rescan variability in the mean upper cervical cord area calculated from T1-weighted cerebral MRIs.

Materials And Methods: In this retrospective study, 8 healthy participants were scanned and rescanned with non-distortion- and distortion-corrected MPRAGE sequences (11-50 sessions in 6-8 months), and 50 participants were scanned once with distortion-corrected MPRAGE sequences in the Day2day daily variability study. From another real-world observational cohort, we collected non-distortion-corrected MPRAGE scans from 27 healthy participants (annually for 2-4 years) and cross-sectionally from 77 participants. Statistical analyses included coefficient of variation, smallest real difference, intraclass correlation coefficient, Bland-Altman limits of agreement, and paired tests.

Results: Distortion- versus non-distortion-corrected MPRAGE-derived mean upper cervical cord areas were similar; however, a paired test showed incomparability ( = 11.0, = <.001). Higher variability was found in the mean upper cervical cord areas calculated from an automatic segmentation method. Interrater analysis yielded incomparable measures in the same participant scans ( = 4.5, 001). Non-distortion-corrected mean upper cervical cord area measures were shown to be robust in real-world data ( = -1.04, = .31). The main sources of variability were found to be artifacts from movement, head/neck positioning, and/or metal implants.

Conclusions: Technical variability in cord measures decreased using non-distortion-corrected MRIs, a semiautomatic segmentation approach, and 1 rater. Rescan variability was within ±4.4% for group mean upper cervical cord area when MR imaging quality criteria were met.

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References

Chien C, Scheel M, Schmitz-Hubsch T, Borisow N, Ruprecht K, Bellmann-Strobl J . Spinal cord lesions and atrophy in NMOSD with AQP4-IgG and MOG-IgG associated autoimmunity. Mult Scler. 2018; 25(14):1926-1936. DOI: 10.1177/1352458518815596. View

Brownlee W, Altmann D, Alves Da Mota P, Swanton J, Miszkiel K, Wheeler-Kingshott C . Association of asymptomatic spinal cord lesions and atrophy with disability 5 years after a clinically isolated syndrome. Mult Scler. 2016; 23(5):665-674. DOI: 10.1177/1352458516663034. View

Lammertse D, Dungan D, Dreisbach J, Falci S, Flanders A, Marino R . Neuroimaging in traumatic spinal cord injury: an evidence-based review for clinical practice and research. J Spinal Cord Med. 2007; 30(3):205-14. PMC: 2031961. DOI: 10.1080/10790268.2007.11753928. View

Yiannakas M, Mustafa A, De Leener B, Kearney H, Tur C, Altmann D . Fully automated segmentation of the cervical cord from T1-weighted MRI using PropSeg: Application to multiple sclerosis. Neuroimage Clin. 2016; 10:71-7. PMC: 4678307. DOI: 10.1016/j.nicl.2015.11.001. View

Weeda M, Middelkoop S, Steenwijk M, Daams M, Amiri H, Brouwer I . Validation of mean upper cervical cord area (MUCCA) measurement techniques in multiple sclerosis (MS): High reproducibility and robustness to lesions, but large software and scanner effects. Neuroimage Clin. 2019; 24:101962. PMC: 6704046. DOI: 10.1016/j.nicl.2019.101962. View

Liu Y, Lukas C, Steenwijk M, Daams M, Versteeg A, Duan Y . Multicenter Validation of Mean Upper Cervical Cord Area Measurements from Head 3D T1-Weighted MR Imaging in Patients with Multiple Sclerosis. AJNR Am J Neuroradiol. 2015; 37(4):749-54. PMC: 7960152. DOI: 10.3174/ajnr.A4635. View

Filippi M, Rocca M, Ciccarelli O, De Stefano N, Evangelou N, Kappos L . MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol. 2016; 15(3):292-303. PMC: 4760851. DOI: 10.1016/S1474-4422(15)00393-2. View

Trefler A, Sadeghi N, Thomas A, Pierpaoli C, Baker C, Thomas C . Impact of time-of-day on brain morphometric measures derived from T1-weighted magnetic resonance imaging. Neuroimage. 2016; 133:41-52. PMC: 5602560. DOI: 10.1016/j.neuroimage.2016.02.034. View

Valsasina P, Aboulwafa M, Preziosa P, Messina R, Falini A, Comi G . Cervical Cord T1-weighted Hypointense Lesions at MR Imaging in Multiple Sclerosis: Relationship to Cord Atrophy and Disability. Radiology. 2018; 288(1):234-244. DOI: 10.1148/radiol.2018172311. View

10.

Ho J, Tumkaya T, Aryal S, Choi H, Claridge-Chang A . Moving beyond P values: data analysis with estimation graphics. Nat Methods. 2019; 16(7):565-566. DOI: 10.1038/s41592-019-0470-3. View

11.

Tustison N, Avants B, Cook P, Zheng Y, Egan A, Yushkevich P . N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010; 29(6):1310-20. PMC: 3071855. DOI: 10.1109/TMI.2010.2046908. View

12.

Beckerman H, Roebroeck M, Lankhorst G, Becher J, Bezemer P, Verbeek A . Smallest real difference, a link between reproducibility and responsiveness. Qual Life Res. 2002; 10(7):571-8. DOI: 10.1023/a:1013138911638. View

13.

Kato F, Yukawa Y, Suda K, Yamagata M, Ueta T . Normal morphology, age-related changes and abnormal findings of the cervical spine. Part II: Magnetic resonance imaging of over 1,200 asymptomatic subjects. Eur Spine J. 2012; 21(8):1499-507. PMC: 3535246. DOI: 10.1007/s00586-012-2176-4. View

14.

Ciccarelli O, Cohen J, Reingold S, Weinshenker B . Spinal cord involvement in multiple sclerosis and neuromyelitis optica spectrum disorders. Lancet Neurol. 2019; 18(2):185-197. DOI: 10.1016/S1474-4422(18)30460-5. View

15.

Wang C, Tam R, Mackie E, Li D, Traboulsee A . Dehydration affects spinal cord cross-sectional area measurement on MRI in healthy subjects. Spinal Cord. 2014; 52(8):616-20. DOI: 10.1038/sc.2014.66. View

16.

Valsasina P, Rocca M, Horsfield M, Copetti M, Filippi M . A longitudinal MRI study of cervical cord atrophy in multiple sclerosis. J Neurol. 2015; 262(7):1622-8. DOI: 10.1007/s00415-015-7754-z. View

17.

Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont S . Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage. 2018; 184:901-915. PMC: 6759925. DOI: 10.1016/j.neuroimage.2018.09.081. View

18.

Horsfield M, Sala S, Neema M, Absinta M, Bakshi A, Sormani M . Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis. Neuroimage. 2010; 50(2):446-55. PMC: 2830007. DOI: 10.1016/j.neuroimage.2009.12.121. View

19.

Prados F, Barkhof F . Spinal cord atrophy rates: Ready for prime time in multiple sclerosis clinical trials?. Neurology. 2018; 91(4):157-158. DOI: 10.1212/WNL.0000000000005873. View

20.

Moccia M, Ruggieri S, Ianniello A, Toosy A, Pozzilli C, Ciccarelli O . Advances in spinal cord imaging in multiple sclerosis. Ther Adv Neurol Disord. 2019; 12:1756286419840593. PMC: 6477770. DOI: 10.1177/1756286419840593. View