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

• Autor(a) principal: EKŞİ,Ziya
• Data de Publicação: 2020
• Outros Autores: ÇAKIROĞLU,Murat, ÖZ,Cemil, ARALAŞMAK,Ayse, KARADELİ,Hasan Hüseyin, ÖZCAN,Muhammed Emin
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Arquivos de neuro-psiquiatria (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0004-282X2020001200789
• topic: Multiple Sclerosis; Multiple Sclerosis, Relapsing-Remitting; Multiple Sclerosis, Chronic Progressive; Magnetic Resonance Spectroscopy; Machine Learning

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.