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Differentiation of multiple sclerosis lesions and low-grade brain tumors on MRS data: machine learning approaches

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Abstract

Some multiple sclerosis (MS) lesions may have great similarities with neoplastic brain lesions in magnetic resonance (MR) imaging and thus wrong diagnoses may occur. In this study, differentiation of MS and low-grade brain tumors was performed with computer-aided diagnosis (CAD) methods by magnetic resonance spectroscopy (MRS) data. MRS data belonging to 51 MS and 39 low-grade brain tumor patients were obtained. The feature extraction from MRS data was performed by the help of peak integration (PI) and full spectra (FS) methods and the most significant features were identified. For the classification step, artificial neural network (ANN), support vector machine (SVM), and linear discriminant analysis (LDA) methods were used and the differentiation between MS and brain tumor was performed automatically. Examining the results, one can conclude that data which belong to MS and low-grade brain tumor cases were automatically differentiated from each other with the help of ANN with 100% accuracy, 100% sensitivity, and 100% specificity. Using of MR spectroscopy and artificial intelligence methods may be useful as a complementary imaging technique to MR imaging in the differentiation of MS lesions and low-grade brain tumors.

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Acknowledgments

This study was supported by Sakarya University BAPK (Project No: 2015-50-02-012). The authors wish to thank all patients included in the study for their approval to the use of their MRS data for research and educational purposes.

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Authors and Affiliations

  1. Department of Computer Engineering, Sakarya University, Sakarya, Turkey

    Ziya Ekşi & Cemil Öz

  2. Department of Neurology, Yeni Yuzyil University, Istanbul, Turkey

    Muhammed Emin Özcan

  3. Department of Electronic Engineering, Sakarya, Turkey

    Murat Çakıroğlu

  4. Department of Radiology, Memorial Bahcelievler Hospital, Istanbul, Turkey

    Ayşe Aralaşmak

Authors
  1. Ziya Ekşi
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  2. Muhammed Emin Özcan
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  3. Murat Çakıroğlu
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  4. Cemil Öz
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  5. Ayşe Aralaşmak
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Correspondence to Ziya Ekşi.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Ekşi, Z., Özcan, M.E., Çakıroğlu, M. et al. Differentiation of multiple sclerosis lesions and low-grade brain tumors on MRS data: machine learning approaches. Neurol Sci 42, 3389–3395 (2021). https://doi.org/10.1007/s10072-020-04950-0

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  • DOI: https://doi.org/10.1007/s10072-020-04950-0

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