A Serial 10-Year Follow-Up Study of Atrophied Brain Lesion Volume and Disability Progression in Patients with Relapsing-Remitting MS

BACKGROUND AND PURPOSE: Disappearance of T2 lesions into CSF spaces is frequently observed in patients with MS. Our aim was to investigate temporal changes of cumulative atrophied brain T2 lesion volume and 10-year confirmed disability progression. MATERIALS AND METHODS: We studied 176 patients with relapsing-remitting MS who underwent MR imaging at baseline, 6 months, and then yearly for 10 years. Occurrence of new/enlarging T2 lesions, changes in T2 lesion volume, and whole-brain, cortical and ventricle volumes were assessed yearly between baseline and 10 years. Atrophied T2 lesion volume was calculated by combining baseline lesion masks with follow-up CSF partial volume maps. Ten-year confirmed disability progression was confirmed after 48 weeks. ANCOVA detected MR imaging outcome differences in stable (n = 76) and confirmed disability progression (n = 100) groups at different time points; hierarchic regression determined the unique additive variance explained by atrophied T2 lesion volume regarding the association with confirmed disability progression, in addition to other MR imaging metrics. Cox regression investigated the association of early MR imaging outcome changes and time to development of confirmed disability progression. RESULTS: The separation of stable-versus-confirmed disability progression groups became significant even in the first 6 months for atrophied T2 lesion volume (140% difference, Cohen d = 0.54, P = .004) and remained significant across all time points (P ≤ .007). The hierarchic model, including all other MR imaging outcomes during 10 years predicting confirmed disability progression, improved significantly after adding atrophied T2 lesion volume (R2 = 0.27, R2 change 0.11, P = .009). In Cox regression, atrophied T2 lesion volume in 0–6 months (hazard ratio = 4.23, P = .04) and 0–12 months (hazard ratio = 2.41, P = .022) was the only significant MR imaging predictor of time to confirmed disability progression. CONCLUSIONS: Atrophied T2 lesion volume is a robust and early marker of disability progression in relapsing-remitting MS.

[1]  D. Ramasamy,et al.  Atrophied Brain Lesion Volume: A New Imaging Biomarker in Multiple Sclerosis , 2018, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[2]  M. Dwyer,et al.  Atrophied brain lesion volume, a magnetic resonance imaging biomarker for monitoring neurodegenerative changes in multiple sclerosis. , 2018, Quantitative imaging in medicine and surgery.

[3]  Robert Zivadinov,et al.  Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year disability in multiple sclerosis , 2017, Multiple sclerosis.

[4]  David H. Miller,et al.  Relationship of grey and white matter abnormalities with distance from the surface of the brain in multiple sclerosis , 2016, Journal of Neurology, Neurosurgery & Psychiatry.

[5]  R. Benedict,et al.  Clinical relevance of brain atrophy assessment in multiple sclerosis. Implications for its use in a clinical routine , 2016, Expert review of neurotherapeutics.

[6]  D. Ramasamy,et al.  A serial 10-year follow-up study of brain atrophy and disability progression in RRMS patients , 2016, Multiple sclerosis.

[7]  J. Duyn,et al.  Heterogeneity of Multiple Sclerosis White Matter Lesions Detected With T2*‐Weighted Imaging at 7.0 Tesla , 2015, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[8]  David H. Miller,et al.  Imaging outcomes for trials of remyelination in multiple sclerosis , 2014, Journal of Neurology, Neurosurgery & Psychiatry.

[9]  H. Wiendl,et al.  Clinical Relevance of Brain Volume Measures in Multiple Sclerosis , 2014, CNS Drugs.

[10]  S. Hussein,et al.  Evolution of Cortical and Thalamus Atrophy and Disability Progression in Early Relapsing-Remitting MS during 5 Years , 2013, American Journal of Neuroradiology.

[11]  M. Dwyer,et al.  Thalamic atrophy is associated with development of clinically definite multiple sclerosis. , 2013, Radiology.

[12]  Thomas Benner,et al.  Contribution of cortical lesion subtypes at 7T MRI to physical and cognitive performance in MS , 2013, Neurology.

[13]  Mark Jenkinson,et al.  The effect of hypointense white matter lesions on automated gray matter segmentation in multiple sclerosis , 2012, Human brain mapping.

[14]  Robert Zivadinov,et al.  Recent Developments in Imaging of Multiple Sclerosis , 2011, The neurologist.

[15]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[16]  J. L. Cox,et al.  Randomized study of interferon beta-1a, low-dose azathioprine, and low-dose corticosteroids in multiple sclerosis , 2009, Multiple sclerosis.

[17]  Sridar Narayanan,et al.  Measuring demyelination and remyelination in acute multiple sclerosis lesion voxels. , 2009, Archives of neurology.

[18]  M. Filippi,et al.  Mechanisms of action of disease-modifying agents and brain volume changes in multiple sclerosis , 2008, Neurology.

[19]  M. Filippi,et al.  Magnetic resonance imaging metrics and their correlation with clinical outcomes in multiple sclerosis: a review of the literature and future perspectives , 2008, Multiple sclerosis.

[20]  Robert Zivadinov,et al.  The place of conventional MRI and newly emerging MRI techniques in monitoring different aspects of treatment outcome , 2008, Journal of Neurology.

[21]  J. L. Cox,et al.  Evolution of different MRI measures in patients with active relapsing-remitting multiple sclerosis over 2 and 5 years: a case–control study , 2007, Journal of Neurology, Neurosurgery, and Psychiatry.

[22]  R. Zivadinov Can imaging techniques measure neuroprotection and remyelination in multiple sclerosis? , 2007, Neurology.

[23]  R. Rudick,et al.  Significance of T2 lesions in multiple sclerosis: A 13‐year longitudinal study , 2006, Annals of neurology.

[24]  Rohit Bakshi,et al.  The measurement and clinical relevance of brain atrophy in multiple sclerosis , 2006, The Lancet Neurology.

[25]  Frederik Barkhof,et al.  Intracortical lesions in multiple sclerosis: improved detection with 3D double inversion-recovery MR imaging. , 2005, Radiology.

[26]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[27]  David H. Miller,et al.  Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. , 2002, Brain : a journal of neurology.

[28]  F. Barkhof The clinico‐radiological paradox in multiple sclerosis revisited , 2002, Current opinion in neurology.

[29]  M. Filippi,et al.  MRI metrics as surrogate markers for clinical relapse rate in relapsing-remitting MS patients , 2002, Neurology.

[30]  Y. Benjamini,et al.  Controlling the false discovery rate in behavior genetics research , 2001, Behavioural Brain Research.

[31]  A J Thompson,et al.  T1 lesion load and cerebral atrophy as a marker for clinical progression in patients with multiple sclerosis. A prospective 18 months follow‐up study , 2001, European journal of neurology.

[32]  F Barkhof,et al.  Neuronal damage in T1‐hypointense multiple sclerosis lesions demonstrated in vivo using proton magnetic resonance spectroscopy , 1999, Annals of neurology.

[33]  B D Trapp,et al.  Axonal pathology in multiple sclerosis: relationship to neurologic disability. , 1999, Current opinion in neurology.

[34]  A. Thompson,et al.  The prognostic value of brain MRI in clinically isolated syndromes of the CNS. A 10-year follow-up. , 1998, Brain : a journal of neurology.

[35]  Jayaram K. Udupa,et al.  New variants of a method of MRI scale standardization , 2000, IEEE Transactions on Medical Imaging.