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

• Published in: Arquivos de neuro-psiquiatria Vol. 78; no. 12; pp. 789 - 796
• Main Authors: Ekşi, Ziya, Çakiroğlu, Murat, Öz, Cemil, Aralaşmak, Ayse, Karadeli, Hasan Hüseyin, Özcan, Muhammed Emin
• Format: Journal Article
• Language: English
• Published: Rua do Matoso 170, Rio de Janeiro, RJ, CEP 20270-135, Brazil Thieme Revinter Publicações Ltda 01.12.2020; Arquivos de Neuro-Psiquiatria; Academia Brasileira de Neurologia - ABNEURO; Thieme Revinter Publicações
• Subjects: Diagnosis; Humans; Learning algorithms; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Metabolites; Multiple Sclerosis; Multiple Sclerosis, Chronic Progressive; Multiple Sclerosis, Chronic Progressive - diagnostic imaging; Multiple Sclerosis, Relapsing-Remitting; Multiple Sclerosis, Relapsing-Remitting - diagnostic imaging; N-Acetylaspartate; NEUROSCIENCES; PSYCHIATRY; Signal processing; Spectrum analysis; Support vector machines; Multiple Sclerosis; Esclerose Múltipla Recidivante-Remitente; Esclerose Múltipla Crônica Progressiva; Ressonância Magnética com espectroscopia; Multiple Sclerosis, Relapsing-Remitting; Multiple Sclerosis, Chronic Progressive; Magnetic Resonance Spectroscopy; Aprendizado de Máquina; Esclerose Múltipla; Machine Learning
• ISSN: 0004-282X; 1678-4227; 1678-4227
• DOI: 10.1590/0004-282X20200094

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.