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CC BY-NC-ND 4.0 · Arq Neuropsiquiatr 2020; 78(12): 789-796
DOI: 10.1590/0004-282X20200094
Article

Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning

Diferenciação de esclerose múltipla recorrente-remitente e progressiva secundária: um estudo de ressonância magnética com espectroscopia baseado em aprendizado de máquina
Ziya EKŞİ 
1   Sakarya University, Department of Computer Engineering, Sakarya, Turkey;
,
Murat ÇAKIROĞLU 
2   Sakarya University, Department of Mechatronic Engineering, Sakarya, Turkey;
,
Cemil ÖZ 
1   Sakarya University, Department of Computer Engineering, Sakarya, Turkey;
,
Ayse ARALAŞMAK 
3   Memorial Bahçelievler Hospital, Department of Radiology, Istanbul, Turkey;
,
Hasan Hüseyin KARADELİ 
4   Istanbul Medeniyet University, Department of Neurology, Istanbul, Turkey;
,
Muhammed Emin ÖZCAN 
5   Istanbul Yeni Yüzyıl University, Department of Neurology, Istanbul, Turkey.
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ABSTRACT

Introduction: Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination of relapsing-remitting MS (RRMS) from secondary progressive MS (SPMS) is clinically difficult, and developing the proposal presented in this study would contribute to the process. Objective: This study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning methods. Methods: MR spectroscopy (MRS) was performed on a total of 91 participants, distributed into healthy controls (n=30), RRMS (n=36), and SPMS (n=25). Firstly, MRS metabolites were identified using signal processing techniques. Secondly, feature extraction was performed based on MRS Spectra. N-acetylaspartate (NAA) was the most significant metabolite in differentiating MS types. Lastly, binary classifications (healthy controls-RRMS and RRMS-SPMS) were carried out according to features obtained by the Support Vector Machine algorithm. Results: RRMS cases were differentiated from healthy controls with 85% accuracy, 90.91% sensitivity, and 77.78% specificity. RRMS and SPMS were classified with 83.33% accuracy, 81.81% sensitivity, and 85.71% specificity. Conclusions: A combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types.

RESUMO

Introdução: A ressonância magnética é a ferramenta mais importante para o diagnóstico e acompanhamento na EM. A transição da EM recorrente-remitente (EMRR) para a EM progressiva secundária (EMPS) é clinicamente difícil e seria importante desenvolver a proposta apresentada neste estudo a fim de contribuir com o processo. Objetivo: o objetivo deste estudo foi garantir a classificação automática de grupo controle saudável, EMRR e EMPS usando a RM com espectroscopia e métodos de aprendizado de máquina. Métodos: Os exames de RM com espectroscopia foram realizados em um total de 91 amostras com grupo controle saudável (n=30), EMRR (n=36) e EMPS (n=25). Em primeiro lugar, os metabólitos da RM com espectroscopia foram identificados usando técnicas de processamento de sinal. Em segundo lugar, a extração de recursos foi realizada a partir do MRS Spectra. O NAA foi determinado como o metabólito mais significativo na diferenciação dos tipos de MS. Por fim, as classificações binárias (Healthy Control Group-RRMS e RRMS-SPMS) foram realizadas de acordo com as características obtidas por meio do algoritmo Support Vector Machine. Resultados: Os casos de EMRR e do grupo de controle saudável foram diferenciados entre si com 85% de acerto, 90,91% de sensibilidade e 77,78% de especificidade, respectivamente. A EMRR e a EMPS foram classificadas com 83,33% de acurácia, 81,81% de sensibilidade e 85,71% de especificidade, respectivamente. Conclusões: Uma análise combinada de RM com espectroscopia e abordagem de diagnóstico auxiliado por computador pode ser útil como uma técnica de imagem complementar na determinação dos tipos de EM.

Keywords:

Multiple Sclerosis - Multiple Sclerosis, Relapsing-Remitting - Multiple Sclerosis, Chronic Progressive - Magnetic Resonance Spectroscopy - Machine Learning

Palavras-chave:

Esclerose Múltipla - Esclerose Múltipla Recidivante-Remitente - Esclerose Múltipla Crônica Progressiva - Ressonância Magnética com espectroscopia - Aprendizado de Máquina

Author's contribution:

Z.E., C.Ö. and M.Ç. designed the algorithm and carried out the implementation. M.E.Ö. and A.A. collected and analyzed Magnetic Resonance Spectroscopy and data. M.E.Ö collected and analyzed clinical data. C.Ö. and Z.E. wrote the manuscript in consultation with M.E.Ö, A.A., H.H.K and M.Ç.


Support:

Sakarya University BAPK (Project No. 2015-50-02-012).




Publication History

Received: 31 May 2020

Accepted: 04 June 2020

Article published online:
07 June 2023

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  • References

  • 1 Lublin FD, Reingold SC, Cohen JA, Cutter GR, Sørensen PS, Thompson AJ, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014 Jul 15;83(3):278-86. https://doi.org/10.1212/WNL.20200094202000940560
  • 2 Freedman MS, Thompson EJ, Deisenhammer F, Giovannoni G, Grimsley G, Keir G, et al. Recommended standard of cerebrospinal fluid analysis in the diagnosis of multiple sclerosis: a consensus statement. Arch Neurol. 2005 Jun;62(6):865-70. https://doi.org/10.1001/archneur.62.6.865
  • 3 Lugue FA, Jaffe SL. Cerebrospinal fluid analysis in multiple sclerosis. Int Rev Neurobiol. 2007 Jun;79:341-56. https://doi.org/10.1016/S0074-7742(07)79015-3
  • 4 Sajja BR, Wolinsky JS, Narayana PA. Proton magnetic resonance spectroscopy in multiple sclerosis. Neuroimaging Clin N Am. 2009 Feb;19(1):45-58. https://doi.org/10.1016/j.nic.2008.08.002
  • 5 Saindane AM, Cha S, Law M, Xue X, Knopp EA, Zagzag D. Proton MR spectroscopy of tumefactive demyelinating lesions. AJNR Am J Neuroradiol. 2002 Sep;23(8):1378-86
  • 6 De Stefano N, Bartolozzi ML, Guidi L, Stromillo ML, Federico A. Magnetic resonance spectroscopy as a measure of brain damage in multiple sclerosis. J Neurol Sci. 2005 Jun 15; 233(1-2):203-8. https://doi.org/10.1016/j.jns.2005.03.018
  • 7 Ge Y. Multiple sclerosis: the role of MR imaging. AJNR Am J Neuroradiol. 2006 Jun-Jul;27(6):1165-76.
  • 8 Vingara LK, Yu HJ, Wagshul ME, Serafin D, Christodoulou C, Pelczer I, et al. Metabolomic approach to human brain spectroscopy identifies associations between clinical features and the frontal lobe metabolome in multiple sclerosis. Neuroimage. 2013 Nov 15;82:586-94. https://doi.org/10.1016/j.neuroimage.2013.05.125
  • 9 Kirov II, Tal A, Babb JS, Herbert J, Gonen O. Serial proton MR spectroscopy of gray and white matter in relapsing-remitting MS. Neurology. 2013 Jan 1;80(1):39-46. https://doi.org/10.1212/WNL.0b013e31827b1a8c
  • 10 Aboul-Enein F. MR spectroscopy in multiple sclerosis - a new piece of the puzzle or just a new puzzle. Magnetic Resonance Spectroscopy. InTech 2012 Mar;3:47-72. https://doi.org/10.5772/32340
  • 11 Abd El-Rahman HM, Hasan DI, Selim HA, Lotfi SM, Elsayed WM. Clinical use of H1MR spectroscopy in assessment of relapsing remitting and secondary progressive multiple sclerosis. Egypt J Radiol Nucl Med. 2012 Jun;43(2)257-64. https://doi.org/10.1016/j.ejrnm.2011.12.009
  • 12 Pan JW, Coyle PK, Bashir K, Whitaker JN, Krupp LB, Hetherington HP. Metabolic differences between multiple sclerosis subtypes measured by quantitative MR spectroscopy. Mult Scler. 2002 May;8(3):200-6. https://doi.org/10.1191/1352458502ms802oa
  • 13 Narayana PA, Wolinsky JS, Rao SB, He R, Mehta M; PROMiSe Trial MRSI Group. Multicentre proton magnetic resonance spectroscopy imaging of primary progressive multiple sclerosis. Mult Scler. 2004 May;10(3):73-8. https://doi.org/10.1191/1352458504ms1035oa
  • 14 Achtnichts L, Gonen O, Rigotti DJ, Babb JS, Naegelin Y, Penner IK, et al. Global N-acetylaspartate concentration in benign and non-benign multiple sclerosis patients of long disease duration. Eur J Radiol. 2013 Dec;82(12):848-52. https://doi.org/10.1016/j.ejrad.2013.08.037
  • 15 Marliani AF, Clementi V, Albini Riccioli L, Agati R, Carpenzano M, Salvi F, et al. Quantitative cervical spinal cord 3T Proton MR spectroscopy in Multiple Sclerosis. Am J Neuroradiol. 2010 Jan;31(1):180-4. https://doi.org/10.3174/ajnr.A1738
  • 16 Vieira BH, Dos Santos AC, Salmon CEG. Pattern recognition of abscesses and brain tumors through MR spectroscopy: Comparison of experimental conditions and radiological findings. Res Biomed Eng. 2017; 33(3):185-194.
  • 17 Olliverre N, Yang G, Slabauhg G, Reyes-Aldasoro CC, Alonso E. Generating magnetic resonance spectroscopy imaging data of brain tumours from linear, non-linear and deep learning models. Springer, Cham. 2018 Sep;11037:130-138. https://doi.org/10.1007/978-3-030-00536-8_14
  • 18 Gurbani SS, Schreibmann E, Maudsley AA, Cordova JS, Soher BJ, Poptani H, et al. A convolutional neural network to filter artifacts in spectroscopic MRI. Magn Reson Med. 2018 Nov;80(5):1765-1775. https://doi.org/10.1002/mrm.27166
  • 19 Ion-Mărgineanu A, Kocevar G, Stamile C, Sima DM, Durand-Dubief F, Van Huffel S, et al. achine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features. Front Neurosci. 2017 Jul 11;11:398. https://doi.org/10.3389/fnins.2017.00398
  • 20 Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011 Feb;69(2):292-302. https://doi.org/10.1002/ana.22366
  • 21 Wilson M, Reynolds G, Kauppinen RA, Arvanitis TN, Peet AC. A constrained least-squares approach to the automated quantitation of in vivo ¹H magnetic resonance spectroscopy data. Magn Reson Med. 2011 Jan;65(1):1-12. https://doi.org/10.1002/mrm.22579
  • 22 Klose U. In vivo proton spectroscopy in presence of eddy currents. Magn Reson Med. 1990 Apr;14(1):26-30. https://doi.org/10.1002/mrm.1910140104
  • 23 Zarinabad N, Abernethy LJ, Avula S, Davies NP, Rodriguez Gutierrez D, Jaspan T, et al. Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1 H-MR spectroscopy-A multi-center study. Magn Reson Med. 2018 Apr;79(4):2359-66. https://doi.org/10.1002/mrm.26837
  • 24 Gill SK. Isingle voxel proton magnetic resonance spectroscopy of childhood brain tumours [internet]. Birmingham: University Of Birmingham; 2013. 256p. [Acesso em: 02 nov. 2020]. Disponível em: Disponível em: https://etheses.bham.ac.uk//id/eprint/4899/1/Gill14PhD.pdf.
  • 25 Fuster-Garcia E, Navarro C, Vicente J, Tortajada S, García-Gómez JM, Sáez C, et al. Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra. MAGMA. 2011 Feb;24(1):35-42. https://doi.org/10.1007/s10334-010-0241-8
  • 26 García-Gómez JM. Brain tumor classification using magnetic resonance spectroscopy. In: Hayat M. (eds) Tumors of the central nervous system. 2011 May;3:5-19. https://doi.org/10.1007/978-94-007-1399-4_2
  • 27 Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998;2:121-67. https://doi.org/10.1023/A:1009715923555
  • 28 Hsu CW, Chang CC, Lin CJ. A practical guide to support vector classification.
  • 29 Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B, Rätsch G. Support Vector Machines and Kernels for Computational Biology. PLoS Comput Biol. 2008 Oct; 4(10):1-10. https://doi.org/10.1371/journal.pcbi.1000173
  • 30 Wong TT. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit. 2013 Sep;48(9):2839-46. https://doi.org/10.1016/j.patcog.2015.03.009
  • 31 De Stefano NJ, Filippi M. MR spectroscopy in multiple sclerosis. J Neurol Sci. 2007 Apr;17:31-35. https://doi.org/10.1111/j.1552-6569.2007.00134.x
  • 32 García-Gómez JM, Luts J, Julià-Sapé M, Krooshof P, Tortajada S, Robledo JV, et al. Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. MAGMA. 2009 Feb;22(1):5-18. https://doi.org/10.1007/s10334-008-0146-y
  • 33 Tsolaki E, Svolos P, Kousi E, Kapsalaki E, Fountas K, Theodorou K, et al. Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data. Int J Comput Assist Radiol Surg. 2013 Sep; 8(5):751-61. https://doi.org/10.1007/s11548-012-0808-0
  • 34 Georgiadis P, Kostopoulos S, Cavouras D, Glotsos D, Kalatzis I, Sifaki K, et al. Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means of pattern recognition. Magn Reson Imaging. 2011 May;29(4):525-35. https://doi.org/10.1016/j.mri.2010.11.006
  • 35 Luts J, Heerschap A, Suykens JA, Van Huffel S. A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection. Artif Intell Med. 2007 Jun;40(2):87-102. https://doi.org/10.1016/j.artmed.2007.02.002
  • 36 Feinstein A, Freeman J, Lo AC. Treatment of progressive multiple sclerosis: what works, what does not, and what is needed. Lancet Neurol. 2015 Feb;14(2):194-207. https://doi.org/10.1016/S1474-4422(14)70231-5