Comparison of visual and automatic quantitative measurement results on 3D volumetric mri in multiple sclerosis patients
Comparison of visual and automatic quantitative measurement results on 3D volumetric mri in multiple sclerosis patients
Multiple sclerosis (MS) is a chronic, demyelinating disease in which magnetic resonance imaging (MRI) is frequently used in the diagnosis and treatment process. Atrophy and plaque counting in the brain can be measured quantitatively with 3-dimensional (3D) MRI examinations. This study aims to determine the results of automatic, quantitative measurements of 3D volumetric MRIs in relapsing-remitting MS (RRMS) patients, to compare the consistency with the visual, semi-quantitative evaluation results made by the radiologists. 46 patients who were diagnosed with RRMS between 01/03/2018 and 31/12/2020 in the neurology outpatient clinic of our hospital, were clinically stable in their follow-up, had at least two 3D MRIs without artifacts constituted the study group. A neuroradiologist, a radiologist experienced in neuroradiology, and VolBrain software evaluated the patients' brain volumes, plaque numbers, and differences in follow-up MRIs. The mean age of 21 female and 25 male patients was 40.4 ± 8.8 years; the mean total brain volume was 1127 ± 137.63 mm3. A high level of agreement was found between the radiologists in terms of whole-brain volume differences between the two MRIs, which was not statistically significant (95.7%; K = -0.002; p = 0.88). There was no agreement between VolBrain and radiologists (K = -0.043; p = 0.333). Regarding the plaque number analysis; a high level and statistically significant agreement among radiologists (87%; K = 0.665; p
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- Gedizlioğlu M, Yumurtaş S, Trakyalı AU, et al. Multipl sklerozda alternatif ve tamamlayıcı tedavi kullanımı: Kesitsel bir anket çalışması. Turk Noroloji Derg. 2015;21:13-15.
- Hüsnü Efendi & Dement Yandım Kuşçu. Multipl Skleroz Tani ve Tedavi̇ Kılavuzu. Galenos Yayınevi, İstanbul, 2018. ISBN: 978-605-89294-9-4
- Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. Neurology. 2014 Jul 15;83:278-86.
- Sastre-Garriga J, Pareto D, Rovira À. Brain atrophy in multiple sclerosis: clinical relevance and technical aspects. Neuroimaging Clin N Am. 2017;27:289-300.
- Akudjedu TN, Nabulsi L, Makelyte M, et al. A comparative study of segmentation techniques for the quantification of brain subcortical volume. Brain Imaging Behav. 2018;12:1678-95.
- Li X, Morgan PS, Ashburner J, Smith J, Rorden C. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods. 2016;264:47-56.
- Manjón JV, Coupé P. Volbrain: An online MRI brain volumetry system. Front Neuroinform. 2016;10(JUL):1-14.
- De Stefano N, Stromillo ML, Giorgio A, et al. Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2016;87:93-9.
- Magon S, Tsagkas C, Gaetano L, et al. Volume loss in the deep gray matter and thalamic subnuclei: a longitudinal study on disability progression in multiple sclerosis. J Neurol. 2020;267:1536-46.
- Hannoun S, Baalbaki M, Haddad R, et al. Gadolinium effect on thalamus and whole brain tissue segmentation. Neuroradiology. 2018;60:1167-73.
- de Sitter A, Verhoeven T, Burggraaff J, et al. Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multicenter cohort. J Neurol. 2020;267:3541-54.
- Guo C, Ferreira D, Fink K, Westman E, Granberg T. Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis. Eur Radiol. 2019;29:1355-64.
- Brune S, Høgestøl EA, Cengija V, et al. LesionQuant for Assessment of MRI in Multiple Sclerosis—A Promising Supplement to the Visual Scan Inspection. Front Neurol. 2020;11:1-10.
- Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17:162-73.
- Todea RA, Lu PJ, Fartaria MJ, et al. Evolution of cortical and white matter lesion load in early-stage multiple sclerosis: correlation with neuroaxonal damage and clinic changes. Front Neurol. 2020;11(September):1-9.
- Lavery AM, Verhey LH, Waldman AT. Outcome measures in relapsingremitting multiple sclerosis: capturing disability and disease progression in clinical trials. Mult Scler Int. 2014;2014:1-13.
- Sormani MP, Bruzzi P. MRI lesions as a surrogate for relapses in multiple sclerosis: A meta-analysis of randomised trials. Lancet Neurol. 2013;12:669-76.
- Yablonskiy DA, Luo J, Sukstanskii AL, et al. Biophysical mechanisms of MRI signal frequency contrast in multiple sclerosis. Proc Natl Acad Sci U S A. 2012;109:14212-7.
- Akaishi T, Takahashi T, Fujihara K, et al. Number of MRI T1-hypointensity corrected by T2/FLAIR lesion volume indicates clinical severity in patients with multiple sclerosis. PLoS One. 2020;15:1-10.
- Dieleman N, Koek HL, Hendrikse J. Short-term mechanisms influencing volumetric brain dynamics. NeuroImage Clin. 2017;16(September):507-513.
- Marciniewicz E, Podgórski P, Sasiadek M, et al. The role of MR volumetry in brain atrophy assessment in multiple sclerosis: A review of the literature. Adv Clin Exp Med. 2019;28:989-99.