Background: Quantitative measures derived from magnetic resonance imaging (MRI) have been widely investigated as non-invasive biomarkers in multiple sclerosis (MS). However, the correlation of single measures with Expanded Disability Status Scale (EDSS) is poor, especially for studies with large population samples.
Objective: To explore the correlation of MRI-derived measures with EDSS through composite MRI scores.
Methods: Magnetic resonance images of 126 patients with relapsing—remitting MS were segmented into white and gray matter, cerebrospinal fluid, T2-hyperintense lesions, gadolinium contrast-enhancing lesions, T1-hypointense lesions (‘black holes’: BH). The volumes and average T2 values for each of these tissues and lesions were calculated and converted to a z-score (in units of standard deviation from the mean). These z-scores were combined to construct composite z-scores, and evaluated against individual z-scores for correlation with EDSS.
Results: Composite scores including relaxation times of different tissues and/or volumetric measures generally correlated more strongly with EDSS than individual measures. The maximum observed correlation of a composite with EDSS was r = 0.344 (p < 0.0001), which is an improvement over the highest-performing single MRI measure (BH; r = 0.298, p < 0.001).
Conclusion: Z-transformation permits construction of composite scores including volumetric and T2-relaxation measures. Inclusion of multiple MRI measures in the composite can provide a broader characterization of the disease process, resulting in more robust correlations with EDSS.
Kurtzke JFA new scale for evaluating disability in multiple sclerosis. Neurology1955; 5: 580-583.
2.
Kurtzke JFRating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology1983; 33: 1444-1452.
3.
Miller DH and Thompson AJNuclear magnetic resonance monitoring of treatment and prediction of outcome in multiple sclerosis. Philos Trans R Soc Lond B Biol Sci1999; 354: 1687-1695.
4.
Wolinsky JS, Narayana PA, Noseworthy JH, Lublin FD, Whitaker JN, Linde A., Gjörstrup P. and Sullivan HCLinomide in relapsing and secondary progressive MS: part II: MRI results. MRI Analysis Center of the University of Texas-Houston, Health Science Center, and the North American Linomide Investigators. Neurology2000; 54: 1734-1741.
5.
McFarland HF , Barkhof F., Antel J. and Miller DHThe role of MRI as a surrogate outcome measure in multiple sclerosis. Mult Scler2002; 8: 40-51.
6.
Li DK, Li MJ, Traboulsee A., Zhao G., Riddehough A. and Paty D.The use of MRI as an outcome measure in clinical trials. Adv Neurol2006; 98: 203-226.
7.
Erickson BJ and Noseworthy JHValue of magnetic resonance imaging in assessing efficacy in clinical trials of multiple sclerosis therapies. Mayo Clin Proc1997; 72: 1080-1089.
8.
Comi G. and Filippi M.Clinical trials in multiple sclerosis: methodological issues. Curr Opin Neurol2005; 18: 245-252.
9.
Bar-Zohar D., Agosta F., Goldstaub D. and Filippi M.Magnetic resonance imaging metrics and their correlation with clinical outcomes in multiple sclerosis: a review of the literature and future perspectives . Mult Scler2008; 14: 719-727.
10.
Zivadinov R.Can imaging techniques measure neuroprotection and remyelination in multiple sclerosis?Neurology2007; 68(22 Suppl 3): S72-S82. (discussion S91-S96.
11.
Zivadinov R. and Leist TPClinical-magnetic resonance imaging correlations in multiple sclerosis. J Neuroimaging2005; 15(4 Suppl): 10S-21S.
Barkhof F.MRI in multiple sclerosis: correlation with Expanded Disability Status Scale (EDSS). Mult Scler1999; 5: 283-286.
14.
Goodin DSMagnetic resonance imaging as a surrogate outcome measure of disability in multiple sclerosis: have we been overly harsh in our assessment?Ann Neurol2006; 59: 597-605.
15.
Pulizzi A., Rovaris M., Judica E., Sormani MP, Martinelli V., Comi G. and Filippi M.Determinants of disability in multiple sclerosis at various disease stages: a multiparametric magnetic resonance study. Arch Neurol2007; 64: 1163-1168.
16.
Benedict RH , Bruce J., Dwyer MG, Weinstock-Guttman B., Tjoa C., Tavazzi E., Munschauer FE and Zivadinov R.Diffusion-weighted imaging predicts cognitive impairment in multiple sclerosis. Mult Scler2007; 13: 722-730.
17.
Tavazzi E., Dwyer MG, Weinstock-Guttman B., Lema J., Bastianello S., Bergamaschi R., Cosi V., Benedict RH, Munschauer FE 3rd and Zivadinov R.Quantitative diffusion weighted imaging measures in patients with multiple sclerosis. Neuroimage2007; 36: 746-754.
18.
Sormani MP, Bruzzi P., Comi G. and Filippi M.MRI metrics as surrogate markers for clinical relapse rate in relapsing-remitting MS patients. Neurology2002; 58: 417-421.
19.
Rudick RA, Lee JC, Simon J. and Fisher E.Significance of T2 lesions in multiple sclerosis: A 13-year longitudinal study . Ann Neurol2006; 60: 236-242.
20.
Kalkers NF, Bergers L., de Groot V., Lazeron RH, van Walderveen MA, Uitdehaag BM, Polman CH and Barkhof F.Concurrent validity of the MS Functional Composite using MRI as a biological disease marker. Neurology2001; 56: 215-219.
21.
Kalkers NF, Bergers E., Castelijns JA, van Walderveen MA, Bot JC, Adér HJ, Polman CH and Barkhof F.Optimizing the association between disability and biological markers in MS . Neurology2001; 57: 1253-1258.
22.
Neema M., Stankiewicz J., Arora A., Guss ZD and Bakshi R.MRI in multiple sclerosis: what’s inside the toolbox?. Neurotherapeutics2007; 4: 602-617.
23.
Martinelli Boneschi F., Rovaris M., Comi G. and Filippi M.The use of magnetic resonance imaging in multiple sclerosis: lessons learned from clinical trials. Mult Scler2004; 10: 341-347.
24.
Wolinsky JS , Narayana PA and Johnson KPUnited States open-label glatiramer acetate extension trial for relapsing multiple sclerosis: MRI and clinical correlates. Multiple Sclerosis Study Group and the MRI Analysis Center. Mult Scler2001; 7: 33-41.
Wolinsky JS, Narayana PA, Johnson KP, et al. The z4 score appears to have a predictive correlation as well that suggests that it might have the potential as a surrogate outcome measure . San Diego, CA: American Academy of Neurology, 2006.
27.
Riahi F., Zijdenbos A., Narayanan S., Arnold D., Francis G., Antel J. and Evans ACImproved correlation between scores on the expanded disability status scale and cerebral lesion load in relapsing-remitting multiple sclerosis. Results of the application of new imaging methods. Brain1998; 121: 1305-1312.
28.
Duncan JS, Bartlett P. and Barker GJTechnique for measuring hippocampal T2 relaxation time. AJNR Am J Neuroradiol1996; 17: 1805-1810.
29.
Hoque R., Ledbetter C., Gonzalez-Toledo E., Misra V., Menon U., Kenner M., Rabinstein AA, Kelley RE, Zivadinov R. and Minagar A.The role of quantitative neuroimaging indices in the differentiation of ischemia from demyelination: an analytical study with case presentation. Int Rev Neurobiol2007; 79: 491-519.
30.
Neema M., Stankiewicz J., Arora A., Dandamudi VS, Batt CE, Guss ZD, Al-Sabbagh A and Bakshi R.T1- and T2-based MRI measures of diffuse gray matter and white matter damage in patients with multiple sclerosis. J Neuroimaging2007; 17(Suppl 1): 16S-21S.
31.
Barbosa S., Blumhardt LD, Roberts N., Lock T. and Edwards RHMagnetic resonance relaxation time mapping in multiple sclerosis: normal appearing white matter and the ‘invisible’ lesion load. Magn Reson Imaging1994; 12: 33-42.
32.
Stevenson VL , Parker GJ, Barker GJ, Birnie K., Tofts PS, Miller DH and Thompson AJVariations in T1 and T2 relaxation times of normal appearing white matter and lesions in multiple sclerosis. J Neurol Sci2000; 178: 81-87.
33.
http://www.fil.ion.ucl.ac.uk/spm/software/spm2/.
34.
Sajja B., Datta S., He R. and Narayana P.Unified approach for multiple sclerosis lesion segmentation on brain MRI. Ann Biomed Eng2006; 34: 142-151.
35.
Datta S., Sajja BR, He R., Wolinsky JS, Gupta RK and Narayana PASegmentation and quantification of black holes in multiple sclerosis. Neuroimage2006; 29: 467-474.
36.
Parry A., et al. White matter and lesion T1 relaxation times increase in parallel and correlate with disability in multiple sclerosis. J Neurol2002; 249: 1279-1286.
37.
Hasan KM, Gupta RK, Santos RM, Wolinsky JS and Narayana PADiffusion tensor fractional anisotropy of the normal-appearing seven segments of the corpus callosum in healthy adults and relapsing-remitting multiple sclerosis patients. J Magn Reson Imaging2005 ; 21: 735-743.
38.
Vrenken H., Pouwels PJ, Ropele S., Knol DL, Geurts JJ, Polman CH, Barkhof F. and Castelijns JAMagnetization transfer ratio measurement in multiple sclerosis normal-appearing brain tissue: limited differences with controls but relationships with clinical and MR measures of disease. Mult Scler2007 ; 13: 708-716.
39.
Bellmann-Strobl J., Stiepani H., Wuerfel J., Bohner G., Paul F., Warmuth C., Aktas O., Wandinger KP, Zipp F. and Klingebiel R.MR spectroscopy (MRS) and magnetisation transfer imaging (MTI), lesion load and clinical scores in early relapsing remitting multiple sclerosis: a combined cross-sectional and longitudinal study. Eur Radiol2009; 19: 2066-2074.
40.
Narayana PAMagnetic resonance spectroscopy in the monitoring of multiple sclerosis. J Neuroimaging2005; 15(4 Suppl): 46S-57S.
41.
Rashid W., Davies GR, Chard DT, Griffin CM, Altmann DR, Gordon R., Thompson AJ and Miller DHIncreasing cord atrophy in early relapsing-remitting multiple sclerosis: a 3 year study. J Neurol Neurosurg Psychiatry2006; 77: 51-55.
42.
Nelson F., Poonawalla AH, Hou P., Huang F., Wolinsky JS and Narayana PAImproved identification of intracortical lesions in multiple sclerosis with phase-sensitive inversion recovery in combination with fast double inversion recovery MR imaging. AJNR Am J Neuroradiol2007; 28: 1645-1649.
43.
Poonawalla AH , Datta S., Juneja V., Nelson F., Wolinsky JS, Cutter G. and Narayana PAInverse dependence between patient population and correlation of composite MRI scores with EDSS in Multiple Sclerosis. Proc. 17th Scientific Meeting of ISMRM at Hawaii, USA, April 2009; Abstract #1149 2009; 17.
44.
Fisher E.Rudick RA, Simon JH, Cutter G., Baier M., Lee JC, Miller D., Weinstock-Guttman B., Mass MK, Dougherty DS and Simonian NAEight-year follow-up study of brain atrophy in patients with MS. Neurology2002; 59: 1412-1420.
45.
Kappos L., Moeri D., Radue EW, Schoetzau A., Schweikert K., Barkhof F., Miller D., Guttmann CR, Weiner HL, Gasperini C. and Filippi M.Predictive value of gadolinium-enhanced magnetic resonance imaging for relapse rate and changes in disability or impairment in multiple sclerosis: a meta-analysis. Gadolinium MRI Meta-analysis Group. Lancet1999; 353: 964.
46.
Paty DW and Li DKInterferon beta-1b is effective in relapsing-remitting multiple sclerosis. II. MRI analysis results of a multicenter, randomized, double-blind, placebo-controlled trial. UBC MS/MRI Study Group and the IFNB Multiple Sclerosis Study Group . Neurology1993; 43: 662-667.
47.
Naismith RT and Cross AHMultiple sclerosis and black holes: connecting the pixels. Arch Neurol2005; 62: 1666-1668.
48.
Rovaris M., Comi G., Ladkani D., Wolinsky JS, Filippi M. and European/Canadian Glatiramer Acetate Study Group.Short-term correlations between clinical and MR imaging findings in relapsing-remitting multiple sclerosis. AJNR Am J Neuroradiol2003; 24: 75-81.
49.
Petkau J., Reingold SC, Held U., Cutter GR, Fleming TR, Hughes MD, Miller DH, McFarland HF and Wolinsky JSMagnetic resonance imaging as a surrogate outcome for multiple sclerosis relapses . Mult Scler2008; 14: 770-778.
50.
Sormani MP, Bonzano L., Roccatagliata L., Cutter GR, Mancardi GL and Bruzzi P.Magnetic resonance imaging as a potential surrogate for relapses in multiple sclerosis: a meta-analytic approach. Ann Neurol2009; 65: 268-275.
51.
Daumer M., Neuhaus A., Morrissey S., Hintzen R. and Ebers GCMRI as an outcome in multiple sclerosis clinical trials. Neurology2009; 72: 705-711.
52.
Noseworthy JH, Vandervoort MK, Wong CJ and Ebers GCInterrater variability with the Expanded Disability Status Scale (EDSS) and Functional Systems (FS) in a multiple sclerosis clinical trial. The Canadian Cooperation MS Study Group. Neurology1990; 40: 971-975.
53.
Goodkin DE, Cookfair D., Wende K., Bourdette D., Pullicino P., Scherokman B. and Whitham R.Inter- and intrarater scoring agreement using grades 1.0 to 3.5 of the Kurtzke Expanded Disability Status Scale (EDSS). Multiple Sclerosis Collaborative Research Group. Neurology1992; 42: 859-863.
54.
Cutter GR, Baier ML, Rudick RA, Cookfair DL, Fischer JS, Petkau J., Syndulko K., Weinshenker BG, Antel JP, Confavreux C., Ellison GW, Lublin F., Miller AE, Rao SM, Reingold S., Thompson A. and Willoughby E.Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain1999; 122(Pt 5): 871-882.
55.
Fischer JS, Rudick RA, Cutter GR and Reingold SCThe Multiple Sclerosis Functional Composite Measure (MSFC): an integrated approach to MS clinical outcome assessment. National MS Society Clinical Outcomes Assessment Task Force. Mult Scler1999; 5: 244-250.
