Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on 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 s...
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| Published in | Arquivos de neuro-psiquiatria Vol. 78; no. 12; pp. 789 - 796 |
|---|---|
| Main Authors | , , , , , |
| 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 |
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| Online Access | Get full text |
| ISSN | 0004-282X 1678-4227 1678-4227 |
| DOI | 10.1590/0004-282X20200094 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. 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.INTRODUCTIONMagnetic 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.This study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning methods.OBJECTIVEThis study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning 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.METHODSMR 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.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.RESULTSRRMS 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.A combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types.CONCLUSIONSA combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types. 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. 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. This study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning 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. 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. A combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types. 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. |
| Abstract_FL | 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. |
| Author | ÖZCAN, Muhammed Emin ÇAKIROĞLU, Murat ARALAŞMAK, Ayse ÖZ, Cemil KARADELİ, Hasan Hüseyin EKŞİ, Ziya |
| AuthorAffiliation | Istanbul Medeniyet University Memorial Bahçelievler Hospital Yeni Yüzyil University Sakarya University |
| AuthorAffiliation_xml | – name: Istanbul Medeniyet University – name: Sakarya University – name: Memorial Bahçelievler Hospital – name: Yeni Yüzyil University |
| Author_xml | – sequence: 1 givenname: Ziya surname: Ekşi fullname: Ekşi, Ziya – sequence: 2 givenname: Murat surname: Çakiroğlu fullname: Çakiroğlu, Murat – sequence: 3 givenname: Cemil surname: Öz fullname: Öz, Cemil – sequence: 4 givenname: Ayse surname: Aralaşmak fullname: Aralaşmak, Ayse – sequence: 5 givenname: Hasan surname: Karadeli middlename: Hüseyin fullname: Karadeli, Hasan Hüseyin – sequence: 6 givenname: Muhammed surname: Özcan middlename: Emin fullname: Özcan, Muhammed Emin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33331515$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3390_sclerosis2030009 crossref_primary_10_1016_j_nicl_2022_103065 crossref_primary_10_1098_rsos_241052 crossref_primary_10_1016_j_heliyon_2024_e30521 |
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| DocumentTitleAlternate | Diferenciação de esclerose múltipla recorrente-remitente e progressiva secundária: um estudo de ressonância magnética com espectroscopia baseado em aprendizado de máquina |
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| Keywords | 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 |
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Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination... ABSTRACT Introduction: Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination... Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination of relapsing-remitting... Introduction: Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination of... |
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| SubjectTerms | 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 |
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| Title | Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning |
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