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

Creative Commons License

Ekşi Z., Çakiroğlu M., Öz C., Aralaşmak A., Karadeli H. H., Özcan M. E.

Arquivos de Neuro-Psiquiatria, cilt.78, sa.12, ss.789-796, 2020 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 78 Sayı: 12
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1590/0004-282x20200094
  • Dergi Adı: Arquivos de Neuro-Psiquiatria
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, EMBASE, MEDLINE, Psycinfo, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.789-796
  • Anahtar Kelimeler: Chronic progressive, Machine learning, Magnetic resonance spectroscopy, Multiple sclerosis, Multiple sclerosis, Multiple sclerosis, Relapsing-remitting
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • İstanbul Yeni Yüzyıl Üniversitesi Adresli: Evet

Özet

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.