Advertisement

Use Case I: Imaging Biomarkers in Neurological Disease. Focus on Multiple Sclerosis

  • Diana M. Sima
  • Dirk Loeckx
  • Dirk Smeets
  • Saurabh Jain
  • Paul M. Parizel
  • Wim Van Hecke
Chapter

Abstract

Imaging is widely used for diagnosis and monitoring of neurological diseases. CT scans are routinely acquired in emergency units in patients with traumatic injuries or stroke. PET imaging has gained a strong foothold in oncology. MRI has become the standard of practice for the diagnosis, follow-up and management of numerous neurological and psychiatric conditions. All of these imaging techniques have in common that, in clinical practice, the images need to be interpreted visually by trained specialists, who are responsible for initial diagnosis or for interpretation of follow-up examinations.

Within the scientific literature there is increasing emphasis on the use of quantitative medical imaging biomarkers, i.e. relevant numerical values that can be extracted from 2D or 3D medical image data sets, using advanced image processing techniques. Many imaging biomarkers, such as volumetric assessment of brain structures, have been shown to have excellent sensitivity and specificity for diagnosis or prognosis of various neurological diseases.

In this chapter, we shall focus on the development of relevant MR imaging biomarkers for patients with multiple sclerosis (MS). However, several of the techniques described below can be generalised to other neurological conditions.

Keywords

Multiple Sclerosis Multiple Sclerosis Patient Expand Disability Status Scale White Matter Lesion Clinically Isolate Syndrome 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Altmann DR, Jasperse B, Barkhof F, Beckmann K, Filippi M, Kappos LD, Molyneux P, Polman CH, Pozzilli C, Thompson AJ, Wagner K, Yousry TA, Miller DH. Sample sizes for brain atrophy outcomes in trials for secondary progressive multiple sclerosis. Neurology. 2009;72(7):595–601.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Arnold DL, Li D, Hohol M, Chakraborty S, Chankowsky J, Alikhani K, Duquette P, Bhan V, Montanera W, Rabinovitch H, Morrish W, Vandorpe R, Guilbert F, Traboulsee A, Kremenchutzky M. Evolving role of MRI in optimizing the treatment of multiple sclerosis: Canadian consensus recommendations. Mult Scler J Exp Transl Clin. 2015;1:1–9.Google Scholar
  3. 3.
    Bakshi R, Minagar A, Jaisani Z, Wolinsky JS. Imaging of multiple sclerosis: role in neurotherapeutics. NeuroRx J Am Soc Exp NeuroTher. 2005;2:277–303.Google Scholar
  4. 4.
    Benedict RHB, Zivadinov R. Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nature reviews. Neurology. 2011;7(6):332–42.PubMedGoogle Scholar
  5. 5.
    Bermel R, Bakshi R. The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol. 2006;5(2):158–70.CrossRefPubMedGoogle Scholar
  6. 6.
    Boyes RG, Rueckert D, Aljabar P, Whitwell J, Schott JM, Hill DLG, Fox NC. Cerebral atrophy measurements using Jacobian integration: comparison with the boundary shift integral. Neuroimage. 2006;32:159–69.CrossRefPubMedGoogle Scholar
  7. 7.
    Calabrese M, Rinaldi F, Grossi P, Gallo P. Cortical pathology and cognitive impairment in multiple sclerosis. Expert Rev Neurother. 2011;11(3):425–32.CrossRefPubMedGoogle Scholar
  8. 8.
    A. Traboulsee, J.H. Simon, L. Stone, E. Fisher, D.E. Jones, A. Malhotra,S.D. Newsome, J. Oh, D.S. Reich, N. Richert, K. Rammohan, O. Khan,E.-W. Radue, C. Ford, J. Halper, and D. Li. Revised Recommendations of the Consortium of MS Centers Task Force for a Standardized MRI Protocol and Clinical Guidelines for the Diagnosis and Follow-Up of Multiple Sclerosis. AJNR Am J Neuroradiol. 2016;37(3):394–401.Google Scholar
  9. 9.
    Compston A, Coles A. Multiple sclerosis. Lancet. 2008;372:1502–17.CrossRefPubMedGoogle Scholar
  10. 10.
    Despotović I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med. 2015;(450341):23. doi: 10.1155/2015/450341.
  11. 11.
    De Stefano N, Airas L, Grigoriadis N, Mattle HP, O’Riordan J, Oreja-Guevara C, Sellebjerg F, Stankoff B, Walczak A, Wiendl H, Kieseier BC. Clinical relevance of brain volume measures in multiple sclerosis. CNS Drugs. 2014;28(2):147–56.CrossRefPubMedGoogle Scholar
  12. 12.
    De Stefano N, Giorgio A, Battaglini M, Rovaris M, Sormani MP, Barkhof F, Korteweg T, Enzinger C, Fazekas F, Calabrese M, Dinacci D, Tedeschi G, Gass A, Montalban X, Rovira A, Thompson A, Comi G, Miller DH, Filippi M. Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes. Neurology. 2010;74(23):1868–76.CrossRefPubMedGoogle Scholar
  13. 13.
    Durand-Dubief F, Belaroussid B, Armspache JP, Dufoura M, Roggeronea S, Vukusica S, Hannounb S, Sappey-Marinierb D, Confavreuxa C, Cotton F. Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques. AJNR Am J Neuroradiol. 2012;33:1918–24.CrossRefPubMedGoogle Scholar
  14. 14.
    Filippi M, Rocca M. MR imaging of gray matter involvement in multiple sclerosis: implications for understanding disease pathophysiology and monitoring treatment efficacy. AJNR Am J Neuroradiol. 2010;31(7):1171–7.CrossRefPubMedGoogle Scholar
  15. 15.
    Filippi M, Rocca M. MRI and cognition in multiple sclerosis. Neurol Sci Off J Ital Neurol Soc Ital Soc Clin Neurophysiol. 2010;31 Suppl 2:S231–4.Google Scholar
  16. 16.
    Filippi M, Rocca M. Preventing brain atrophy should be the gold standard of effective therapy in MS (after the first year of treatment): No. Mult Scler (Houndmills, Basingstoke, England). 2013;19(8):1005–6.CrossRefGoogle Scholar
  17. 17.
    Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774–81. doi: 10.1016/j.neuroimage.2012.01.021.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Fisniku LK, Brex PA, Altmann DR, Miszkiel KA, Benton CE, Lanyon R, Thompson AJ, Miller DH. Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis. Brain. 2008;131(3):808–17.CrossRefPubMedGoogle Scholar
  19. 19.
    Freeborough PA, Fox NC. The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Trans Med Imaging. 1997;16(5):623–9.CrossRefPubMedGoogle Scholar
  20. 20.
    Freedman MS, Selchen D, Arnold DL, Prat A, Banwell B, Yeung M, Morgenthau D, Lapierre Y, On Behalf Of The Canadian Multiple Sclerosis Working Group. Treatment optimization in MS: Canadian MS working group updated recommendations. Can J Neurol Sci Le Journal Canadien Des Sciences Neurologiques. 2013;40:307–23.CrossRefGoogle Scholar
  21. 21.
    García-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal. 2013;17:1–18.CrossRefPubMedGoogle Scholar
  22. 22.
    Geremia E, Clatz O, Menze BH, Konukoglu E, Criminisi A, Ayache N. Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage. 2011;57:378–90.CrossRefPubMedGoogle Scholar
  23. 23.
    Geurts JJG, Calabrese M, Fisher E, Rudick RA. Measurement and clinical effect of grey matter pathology in multiple sclerosis. Lancet Neurol. 2012;11(12):1082–92.CrossRefPubMedGoogle Scholar
  24. 24.
    Giorgio A, De Stefano N. Cognition in multiple sclerosis: relevance of lesions, brain atrophy and proton MR spectroscopy. Neurol Sci Off J Ital Neurol Soc Ital Soc Clin Neurophysiol. 2010;31 Suppl 2:S245–8.Google Scholar
  25. 25.
    Giorgio A, De Stefano N. Clinical use of brain volumetry. J Magn Reson Imaging. 2013;37(1):1–14.CrossRefPubMedGoogle Scholar
  26. 26.
    Giorgio A, Stromillo ML, Bartolozzi ML, Rossi F, Battaglini M, De Leucio A, Guidi L, Maritato P, Portaccio E, Sormani MP, Amato MP, De Stefano N. Relevance of hypointense brain MRI lesions for long-term worsening of clinical disability in relapsing multiple sclerosis. Mult Scler. 2014;20(2):214–9.CrossRefPubMedGoogle Scholar
  27. 27.
    Hyland M, Rudick RA. Challenges to clinical trials in multiple sclerosis: outcome measures in the era of disease-modifying drugs. Curr Opin Neurol. 2011;24(3):255–61.CrossRefPubMedGoogle Scholar
  28. 28.
    Iglesias JE, Liu CY, Thompson PM, Tu ZW. Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans Med Imaging. 2011;30:1617–34.CrossRefPubMedGoogle Scholar
  29. 29.
    Inglese M, Grossman RI, Filippi M. Magnetic resonance imaging monitoring of multiple sclerosis lesion evolution. J Neuroimaging Off J Am Soc Neuroimaging. 2005;15(4 Suppl):22S–9.CrossRefGoogle Scholar
  30. 30.
    Jacobsen C, Hagemeier J, Myhr K-M, Nyland H, Lode K, Bergsland N, Ramasamy DP, Dalaker TO, Larsen JP, Farbu E, Zivadinov R. Brain atrophy and disability progression in multiple sclerosis patients: a 10-year follow-up study. J Neurol Neurosurg Psychiatry. 2014;85(10):1109–15.CrossRefPubMedGoogle Scholar
  31. 31.
    Jain S, Sima DM, Ribbens A, Cambron M, Maertens A, Van Hecke W, De Mey J, Barkhof F, Steenwijk MD, Daams M, Maes F, Van Huffel S, Vrenken H, Smeets D. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. Neuroimage Clin. 2015;8:367–75. doi: 10.1016/j.nicl.2015.05.003.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Khayati R, Vafadust M, Towhidkhah F, Nabavi M. Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model. Comput Biol Med. 2008;38:379–90.CrossRefPubMedGoogle Scholar
  33. 33.
    Lao Z, Shen D, Liu D, Jawad AF, Melhem ER, Launer LJ, Bryan RN, Davatzikos C. Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Acad Radiol. 2008;15:300–13.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Lavery AM, Verhey LH, Waldman AT. Outcome measures in relapsing-remitting multiple sclerosis: capturing disability and disease progression in clinical trials. Mult Scler Int. 2014;2014:262350.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Lladó X, Ganiler O, Oliver A, Martí R, Freixenet J, Valls L, Vilanova JC, Ramió-Torrentà L, Rovira A. Automated detection of multiple sclerosis lesions in serial brain MRI. Neuroradiology. 2012;54(8):787–807.CrossRefPubMedGoogle Scholar
  36. 36.
    Morgan CJ, Ranjan A, Aban IB, Cutter GR. The magnetic resonance imaging “rule of five”: predicting the occurrence of relapse. Mult Scler (Houndmills, Basingstoke, England). 2013;19(13):1760–4.CrossRefGoogle Scholar
  37. 37.
    Mortazavi D, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology. 2012;54(4):299–320.CrossRefPubMedGoogle Scholar
  38. 38.
    Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O’Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69:292–302.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Popescu V, Agosta F, Hulst HE, Sluimer IC, Knol DL, Sormani MP, Enzinger C, Ropele S, Alonso J, Sastre-Garriga J, Rovira A, Montalban X, Bodini B, Ciccarelli O, Khaleeli Z, Chard DT, Matthews L, Palace J, Giorgio A, De Stefano N, Eisele P, Gass A, Polman CH, Uitdehaag BM, Messina MJ, Comi G, Filippi M, Barkhof F, Vrenken H, MAGNIMS Study Group. Brain atrophy and lesion load predict long term disability in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2013;84(10):1082–91.CrossRefPubMedGoogle Scholar
  40. 40.
    Radü EW, Bendfeldt K, Mueller-Lenke N, Magon S, Sprenger T. Brain atrophy: an in-vivo measure of disease activity in multiple sclerosis. Swiss Med Wkly. 2013;143(November):w13887.PubMedGoogle Scholar
  41. 41.
    Rao SM, Martin AL, Huelin R, Wissinger E, Khankhel Z, Kim E, Fahrbach K. Correlations between MRI and information processing speed in MS: a meta-analysis. Mult Scler Int. 2014;2014:975803.PubMedPubMedCentralGoogle Scholar
  42. 42.
    Richards JE, Sanchez C, Phillips-Meek M, Xie W. A database of age-appropriate average MRI templates. Neuroimage. 2016;124(Pt B):1254–9. doi: 10.1016/j.neuroimage.2015.04.055.CrossRefPubMedGoogle Scholar
  43. 43.
    Riley C, Azevedo C, Bailey M, Pelletier D. Clinical applications of imaging disease burden in multiple sclerosis: MRI and advanced imaging techniques. Expert Rev Neurother. 2012;12(3):323–33.CrossRefPubMedGoogle Scholar
  44. 44.
    Rocca M, Anzalone N, Falini A, Filippi M. Contribution of magnetic resonance imaging to the diagnosis and monitoring of multiple sclerosis. Radiol Med. 2013;118(2):251–64.CrossRefPubMedGoogle Scholar
  45. 45.
    Rovira À, Auger C, Alonso J. Magnetic resonance monitoring of lesion evolution in multiple sclerosis. Ther Adv Neurol Disord. 2013;6(5):298–310.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Rovira À, Wattjes MP, Tintoré M, Tur C, Yousry TA, Sormani MP, De Stefano N, Filippi M, Auger C, Rocca MA, Barkhof F, Fazekas F, Kappos L, Polman C, Miller D, Montalban X, MAGNIMS study group. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-clinical implementation in the diagnostic process. Nat Rev Neurol. 2015;11(8):471–82. doi: 10.1038/nrneurol.2015.106.CrossRefPubMedGoogle Scholar
  47. 47.
    Rudick R, Weinshenker B, Cutter G. Therapeutic considerations: rating scales. In: Cook SD, editors. Handbook of multiple sclerosis. 3rd ed. ISBN 9780824741846 – CAT# DKE276. Series: neurological disease and therapy. CRC Press; New York – Basel. 2001.Google Scholar
  48. 48.
    Rudick R, Fisher E. Preventing brain atrophy should be the gold standard of effective therapy in MS (after the first year of treatment): Yes. Mult Scler (Houndmills, Basingstoke, England). 2013;19(8):1003–4.CrossRefGoogle Scholar
  49. 49.
    Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, Hoshi M, Ilg R, Schmid VJ, Zimmer C, Hemmer B, Mühlau M. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2010;59(4):3774–83.CrossRefGoogle Scholar
  50. 50.
    Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM. Magnetic resonance image tissue classification using a partial volume model. Neuroimage. 2001;13:856–76.CrossRefPubMedGoogle Scholar
  51. 51.
    Shi J, Baxter LC, Kuniyoshi SM. Pathologic and imaging correlates of cognitive deficits in multiple sclerosis: changing the paradigm of diagnosis and prognosis. Cogn Behav Neurol Off J Soc Behav Cogn Neurol. 2014;27:1–7.CrossRefGoogle Scholar
  52. 52.
    Shiee N, Bazin PL, Ozturk A, Reich DS, Calabresi PA, Pham DL. A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage. 2010;49(2):1524–35.CrossRefPubMedGoogle Scholar
  53. 53.
    Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17(1):87–97.CrossRefPubMedGoogle Scholar
  54. 54.
    Smeets D, Ribbens A, Sima DM, Cambron M, Horakova D, Jain S, Van Vlierberghe E, Terzopoulos V, Maertens A, Van Binst AM, Vaneckova M, Krasensky J, Uher T, Seidl Z, De Keyser J, Nagels G, De Mey J, Havrdova E, Van Hecke W. Reliable measurements of brain atrophy in individual patients with Multiple Sclerosis. Hum. Brain Mapp. 2016, 00: 1–12.e00518. doi: 10.1002/brb3.518.
  55. 55.
    Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17:143–55.CrossRefPubMedGoogle Scholar
  56. 56.
    Smith SM, Zhang YY, Jenkinson M, Chen J, Matthews PM, Federico A, De Stefano N. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage. 2002;17:479–89.CrossRefPubMedGoogle Scholar
  57. 57.
    Steenwijk MD, Pouwels PJ, Daams M, van Dalen JW, Caan MW, Richard E, Barkhof F, Vrenken H. Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). Neuroimage Clin. 2013;4(3):462–9. doi: 10.1016/j.nicl.2013.10.003.CrossRefGoogle Scholar
  58. 58.
    Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC. N4ITK: Improved N3 Bias Correction. IEEE Trans Med Imaging. 2010;29(6):1310–20. doi: 10.1109/TMI.2010.2046908.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Vrenken H, Jenkinson M, Horsfield M, Battaglini M, van Schijndel RA, Rostrup E, Geurts JJ, Fisher E, Zijdenbos A, Ashburner J, Miller DH, Filippi M, Fazekas F, Rovaris M, Rovira A, Barkhof F, De Stefano N, MAGNIMS Study Group. Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis. J Neurol. 2013;260(10):2458–71. doi: 10.1007/s00415-012-6762-5.CrossRefPubMedGoogle Scholar
  60. 60.
    Wattjes MP, Rovira À, Miller D, Yousry TA, Sormani MP, De Stefano N, Tintoré M, Auger C, Tur C, Filippi M, Rocca MA, Fazekas F, Kappos L, Polman C, Barkhof F, Montalban X, on behalf of the MAGNIMS study group. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—establishing disease prognosis and monitoring patients. Nat Rev Neurol. 2015;11:597–606.CrossRefPubMedGoogle Scholar
  61. 61.
    Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20(1):45–57.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Diana M. Sima
    • 1
  • Dirk Loeckx
    • 1
  • Dirk Smeets
    • 1
  • Saurabh Jain
    • 1
  • Paul M. Parizel
    • 2
  • Wim Van Hecke
    • 1
  1. 1.icometrixLeuvenBelgium
  2. 2.Department of RadiologyAntwerp University Hospital, University of AntwerAntwerpBelgium

Personalised recommendations