MR Spectroscopy in Multiple Sclerosis - A New Piece of the Puzzle or Just a New Puzzle

Aboul‐Enein, Fahmy

doi:10.5772/32340

Cited by 1 publication

(2 citation statements)

References 112 publications

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“…At the same time, the Cho peak showed no significant changes 11 . Similarly, a study presented by Aboul-Enein reported decreases in NAA, Cho, and Cr peaks in parallel with increasing disease severity 10 . In our study, the most significant change was observed in the NAA peak.…”

Section: Resultssupporting

confidence: 54%

“…Furthermore, N-acetylaspartate (NAA) peak decreases when an orientation from RRMS to SPMS occurs during the course of the disease. NAA peaks were shortened when RRMS switched to SPMS during the MS course 10,11 . Pan et al determined MRS metabolic values in RRMS, SPMS, and PPMS groups and calculated metabolic changes by statistical methods 12 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ekşi

Çakıroğlu

Öz

et al. 2020

Arq. Neuro-Psiquiatr.

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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.

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Section: Resultssupporting

confidence: 54%