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Improved Turnaround Times | Median time to first decision: 12 days

Research ArticleAdult Brain
Open Access

An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions

J.D. Dworkin, K.A. Linn, I. Oguz, G.M. Fleishman, R. Bakshi, G. Nair, P.A. Calabresi, R.G. Henry, J. Oh, N. Papinutto, D. Pelletier, W. Rooney, W. Stern, N.L. Sicotte, D.S. Reich and R.T. Shinohara the North American Imaging in Multiple Sclerosis Cooperative
American Journal of Neuroradiology April 2018, 39 (4) 626-633; DOI: https://doi.org/10.3174/ajnr.A5556
J.D. Dworkin
aFrom the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.)
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K.A. Linn
aFrom the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.)
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I. Oguz
bRadiology (I.O., G.M.F.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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G.M. Fleishman
bRadiology (I.O., G.M.F.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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R. Bakshi
cLaboratory for Neuroimaging Research (R.B.), Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases
dDepartments of Neurology (R.B.)
eRadiology (R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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G. Nair
fTranslational Neuroradiology Section (G.N., D.S.R.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
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P.A. Calabresi
gDepartment of Neurology (P.A.C., J.O., D.S.R.), the Johns Hopkins University School of Medicine, Baltimore, Maryland
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R.G. Henry
hDepartment of Neurology (R.G.H., N.P., W.S.), University of California, San Francisco, San Francisco, California
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J. Oh
gDepartment of Neurology (P.A.C., J.O., D.S.R.), the Johns Hopkins University School of Medicine, Baltimore, Maryland
iKeenan Research Centre for Biomedical Science (J.O.), St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
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N. Papinutto
hDepartment of Neurology (R.G.H., N.P., W.S.), University of California, San Francisco, San Francisco, California
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D. Pelletier
jDepartment of Neurology (D.P.), Keck School of Medicine, University of Southern California, Los Angeles, California
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W. Rooney
kAdvanced Imaging Research Center (W.R.), Oregon Health & Science University, Portland, Oregon
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W. Stern
hDepartment of Neurology (R.G.H., N.P., W.S.), University of California, San Francisco, San Francisco, California
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N.L. Sicotte
lDepartment of Neurology (N.L.S.), Cedars-Sinai Medical Center, Los Angeles, California. A complete list of the NAIMS participants is provided in the acknowledgment section.
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D.S. Reich
fTranslational Neuroradiology Section (G.N., D.S.R.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
gDepartment of Neurology (P.A.C., J.O., D.S.R.), the Johns Hopkins University School of Medicine, Baltimore, Maryland
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R.T. Shinohara
aFrom the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.)
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References

  1. 1.↵
    1. Radü EW,
    2. Sahraian MA
    , eds. MRI Atlas of MS Lesions. Berlin: Springer-Verlag; 2008
  2. 2.↵
    1. Barkhof F
    . The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol 2002;15:239–45 doi:10.1097/00019052-200206000-00003 pmid:12045719
    CrossRefPubMedWeb of Science
  3. 3.↵
    1. Popescu V,
    2. Agosta F,
    3. Hulst HE, et al
    ; MAGNIMS Study Group. Brain atrophy and lesion load predict long term disability in multiple sclerosis. J Neurol Neurosurg Psychiatry 2013;84:1082–91 doi:10.1136/jnnp-2012-304094 pmid:23524331
    Abstract/FREE Full Text
  4. 4.↵
    1. Calabresi PA,
    2. Radue EW,
    3. Goodin D, et al
    . Safety and efficacy of fingolimod in patients with relapsing-remitting multiple sclerosis (FREEDOMS II): a double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Neurol 2014;13:545–56 doi:10.1016/S1474-4422(14)70049-3 pmid:24685276
    CrossRefPubMedWeb of Science
  5. 5.↵
    1. Thompson AJ,
    2. Kermode AG,
    3. MacManus DG, et al
    . Patterns of disease activity in multiple sclerosis: clinical and magnetic resonance imaging study. BMJ. 1990;300:631–34 doi:10.1136/bmj.300.6725.631 pmid:2138923
    Abstract/FREE Full Text
  6. 6.↵
    1. Brex PA,
    2. Ciccarelli O,
    3. O'Riordan JI, et al
    . A longitudinal study of abnormalities on MRI and disability from multiple sclerosis. N Engl J Med 2002;346:158–64 doi:10.1056/NEJMoa011341 pmid:11796849
    CrossRefPubMedWeb of Science
  7. 7.↵
    1. Khoury SJ,
    2. Guttmann CR,
    3. Orav EJ, et al
    . Longitudinal MRI in multiple sclerosis: correlation between disability and lesion burden. Neurology 1994;44:2120–24 doi:10.1212/WNL.44.11.2120 pmid:7969970
    Abstract/FREE Full Text
  8. 8.↵
    1. Rudick RA,
    2. Lee JC,
    3. Simon J, et al
    . Significance of T2 lesions in multiple sclerosis: a 13-year longitudinal study. Ann Neurol 2006;60:236–42 doi:10.1002/ana.20883 pmid:16786526
    CrossRefPubMedWeb of Science
  9. 9.↵
    1. Zivadinov R,
    2. Zorzon M,
    3. De Masi R, et al
    . Effect of intravenous methylprednisolone on the number, size and confluence of plaques in relapsing-remitting multiple sclerosis. J Neurol Sci 2008;267:28–35 doi:10.1016/j.jns.2007.09.025 pmid:17945260
    CrossRefPubMed
  10. 10.↵
    1. Harris JO,
    2. Frank JA,
    3. Patronas N, et al
    . Serial gadolinium-enhanced magnetic resonance imaging scans in patients with early, relapsing-remitting multiple sclerosis: implications for clinical trials and natural history. Ann Neurol 1991;29:548–55 doi:10.1002/ana.410290515 pmid:1859184
    CrossRefPubMedWeb of Science
  11. 11.↵
    1. Guttmann CR,
    2. Rousset M,
    3. Roch JA, et al
    . Multiple sclerosis lesion formation and early evolution revisited: a weekly high-resolution magnetic resonance imaging study. Mult Scler J 2016;22:761–69 doi:10.1177/1352458515600247 pmid:26362901
    CrossRefPubMed
  12. 12.↵
    1. Gaitán MI,
    2. Shea CD,
    3. Evangelou IE, et al
    . Evolution of the blood-brain barrier in newly forming multiple sclerosis lesions. Ann Neurol 2011;70:22–29 doi:10.1002/ana.22472 pmid:21710622
    CrossRefPubMed
  13. 13.↵
    1. Sweeney EM,
    2. Shinohara RT,
    3. Dewey BE, et al
    . Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions. Neuroimage Clin 2016;10:1–17 doi:10.1016/j.nicl.2015.10.013 pmid:26693397
    CrossRefPubMed
  14. 14.↵
    1. Fonov V,
    2. Evans AC,
    3. Botteron K, et al
    ; Brain Development Cooperative Group. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 2011;54:313–27 doi:10.1016/j.neuroimage.2010.07.033 pmid:20656036
    CrossRefPubMedWeb of Science
  15. 15.↵
    1. Avants B,
    2. Tustison N,
    3. Song G, et al
    . ANTS: Advanced Open-Source Normalization Tools for Neuroanatomy. Philadelphia: Penn Image Computing and Science Laboratory; 2009
  16. 16.↵
    1. Carass A,
    2. Wheeler MB,
    3. Cuzzocreo J, et al
    . A joint registration and segmentation approach to skull stripping. In: Proceedings of the 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, Virgina. April 12–15, 2007:656–59. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4193371. Accessed April 1, 2016.
  17. 17.↵
    1. Shinohara RT,
    2. Sweeney EM,
    3. Goldsmith J, et al
    ; Australian Imaging Biomarkers Lifestyle Flagship Study of Ageing, Alzheimer's Disease Neuroimaging Initiative. Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin 2014;6:9–19 doi:10.1016/j.nicl.2014.08.008 pmid:25379412
    CrossRefPubMed
  18. 18.↵
    1. Sweeney EM,
    2. Shinohara RT,
    3. Shiee N, et al
    . OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI. Neuroimage Clin 2013;2:402–13 doi:10.1016/j.nicl.2013.03.002 pmid:24179794
    CrossRefPubMed
  19. 19.↵
    1. Shinohara RT,
    2. Oh J,
    3. Nair G, et al
    ; NAIMS Cooperative. Volumetric analysis from a harmonized multisite brain MRI study of a single subject with multiple sclerosis. AJNR Am J Neuroradiol 2017;38:1501–09 doi:10.3174/ajnr.A5254 pmid:28642263
    Abstract/FREE Full Text
  20. 20.↵
    1. Jenkinson M,
    2. Pechaud M,
    3. Smith S
    . BET2: MR-based estimation of brain, skull and scalp surfaces. Oxford: Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB); 2005. http://mickaelpechaud.free.fr/these/HBM05.pdf. Accessed June 13, 2016.
  21. 21.↵
    1. Valcarcel AN,
    2. Linn KA,
    3. Vandekar SN, et al
    . MIMoSA: a method for inter-modal segmentation analysis. June 15, 2017. bioRxiv 150284 doi:10.1101/150284
  22. 22.↵
    1. Sweeney EM,
    2. Shinohara RT,
    3. Shea CD, et al
    . Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI. AJNR Am J Neuroradiol 2013;34:68–73 doi:10.3174/ajnr.A3172 pmid:22766673
    Abstract/FREE Full Text
  23. 23.↵
    1. Papinutto N,
    2. Bakshi R,
    3. Bischof A, et al
    ; North American Imaging in Multiple Sclerosis Cooperative (NAIMS). Gradient nonlinearity effects on upper cervical spinal cord area measurement from 3D T1-weighted brain MRI acquisitions. Magn Reson Med 2018;79:1595–601 doi:10.1002/mrm.26776 pmid:28617996
    CrossRefPubMed
  24. 24.↵
    1. Oh J,
    2. Bakshi R,
    3. Calabresi PA
    ; NAIMS Cooperative Steering Committee. The NAIMS cooperative pilot project: Design, implementation and future directions. Mult Scler 2017 Oct 1. [Epub ahead of print] doi:10.1177/1352458517739990 pmid:29106329
    CrossRefPubMed
  25. 25.↵
    1. Meier DS,
    2. Guttmann CR
    . Time-series analysis of MRI intensity patterns in multiple sclerosis. Neuroimage 2003;20:1193–209 doi:10.1016/S1053-8119(03)00354-9 pmid:14568488
    CrossRefPubMed
  26. 26.↵
    1. Meier DS,
    2. Guttmann CR
    . MRI time series modeling of MS lesion development. Neuroimage 2006;32:531–37 doi:10.1016/j.neuroimage.2006.04.181 pmid:16806979
    CrossRefPubMed
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J.D. Dworkin, K.A. Linn, I. Oguz, G.M. Fleishman, R. Bakshi, G. Nair, P.A. Calabresi, R.G. Henry, J. Oh, N. Papinutto, D. Pelletier, W. Rooney, W. Stern, N.L. Sicotte, D.S. Reich, R.T. Shinohara
An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions
American Journal of Neuroradiology Apr 2018, 39 (4) 626-633; DOI: 10.3174/ajnr.A5556

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An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions
J.D. Dworkin, K.A. Linn, I. Oguz, G.M. Fleishman, R. Bakshi, G. Nair, P.A. Calabresi, R.G. Henry, J. Oh, N. Papinutto, D. Pelletier, W. Rooney, W. Stern, N.L. Sicotte, D.S. Reich, R.T. Shinohara
American Journal of Neuroradiology Apr 2018, 39 (4) 626-633; DOI: 10.3174/ajnr.A5556
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