Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks

Harish Poptani, Jouni Kaartinen, Rakesh Gupta, Matthias Niemitz, Yrjö Hiltunen, Risto Kauppinen

    Research output: Contribution to journalArticleScientificpeer-review

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    Abstract

    Purpose: Experiments were carried out to assess the potential of artificial neural network (ANN) analysis in the differential diagnosis of brain tumours (low- and high-grade gliomas) from non-neoplastic focal brain lesions (tuberculomas and abscesses), using proton magnetic resonance spectroscopy (1H MRS) as input data. Methods: Single-voxel stimulated echo acquisition mode (STEAM) (echo time of 20 ms) spectra were acquired from 138 subjects including 15 with low-grade gliomas, 47 with high-grade gliomas, 18 with tuberculomas, 18 with abscesses and 40 healthy controls. Two neural networks were constructed using the spectral points from 0.6 to 3.4 parts per million. In the first network construction, the ANN had to differentiate between tumours from infections, while the second network had to differentiate between all five histological classes. Results: ANN analysis gave a histologically correct diagnosis for low- and high-grade gliomas with an accuracy of 73% and 98% respectively. None of the 62 tumours was diagnosed as an infectious lesion. Among the non-neoplastic lesions, ANN classification was correct in 89% of tuberculomas and in 83% of brain abscesses. The specificity of ANN diagnosis was 98%, 92%, 99%, and 100% for low-grade gliomas, high-grade gliomas, tuberculomas and abscesses respectively. Conclusion: The present data show the clinical utility of non-invasive 1H MRS by automated ANN analysis in the diagnosis of tumour and non-tumour cerebral disorders.
    Original languageEnglish
    Pages (from-to)343-349
    Number of pages7
    JournalJournal of Cancer Research and Clinical Oncology
    Volume125
    Issue number6
    DOIs
    Publication statusPublished - 1999
    MoE publication typeA1 Journal article-refereed

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    Brain Diseases
    Brain Neoplasms
    Glioma
    Tuberculoma
    Magnetic Resonance Spectroscopy
    Abscess
    Neoplasms
    Brain Abscess
    Proton Magnetic Resonance Spectroscopy
    Differential Diagnosis
    Brain
    Infection

    Cite this

    @article{c352d732089e44a1a80a0680d629a579,
    title = "Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks",
    abstract = "Purpose: Experiments were carried out to assess the potential of artificial neural network (ANN) analysis in the differential diagnosis of brain tumours (low- and high-grade gliomas) from non-neoplastic focal brain lesions (tuberculomas and abscesses), using proton magnetic resonance spectroscopy (1H MRS) as input data. Methods: Single-voxel stimulated echo acquisition mode (STEAM) (echo time of 20 ms) spectra were acquired from 138 subjects including 15 with low-grade gliomas, 47 with high-grade gliomas, 18 with tuberculomas, 18 with abscesses and 40 healthy controls. Two neural networks were constructed using the spectral points from 0.6 to 3.4 parts per million. In the first network construction, the ANN had to differentiate between tumours from infections, while the second network had to differentiate between all five histological classes. Results: ANN analysis gave a histologically correct diagnosis for low- and high-grade gliomas with an accuracy of 73{\%} and 98{\%} respectively. None of the 62 tumours was diagnosed as an infectious lesion. Among the non-neoplastic lesions, ANN classification was correct in 89{\%} of tuberculomas and in 83{\%} of brain abscesses. The specificity of ANN diagnosis was 98{\%}, 92{\%}, 99{\%}, and 100{\%} for low-grade gliomas, high-grade gliomas, tuberculomas and abscesses respectively. Conclusion: The present data show the clinical utility of non-invasive 1H MRS by automated ANN analysis in the diagnosis of tumour and non-tumour cerebral disorders.",
    author = "Harish Poptani and Jouni Kaartinen and Rakesh Gupta and Matthias Niemitz and Yrj{\"o} Hiltunen and Risto Kauppinen",
    year = "1999",
    doi = "10.1007/s004320050284",
    language = "English",
    volume = "125",
    pages = "343--349",
    journal = "Journal of Cancer Research and Clinical Oncology",
    issn = "0171-5216",
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    }

    Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks. / Poptani, Harish; Kaartinen, Jouni; Gupta, Rakesh; Niemitz, Matthias; Hiltunen, Yrjö; Kauppinen, Risto.

    In: Journal of Cancer Research and Clinical Oncology, Vol. 125, No. 6, 1999, p. 343-349.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks

    AU - Poptani, Harish

    AU - Kaartinen, Jouni

    AU - Gupta, Rakesh

    AU - Niemitz, Matthias

    AU - Hiltunen, Yrjö

    AU - Kauppinen, Risto

    PY - 1999

    Y1 - 1999

    N2 - Purpose: Experiments were carried out to assess the potential of artificial neural network (ANN) analysis in the differential diagnosis of brain tumours (low- and high-grade gliomas) from non-neoplastic focal brain lesions (tuberculomas and abscesses), using proton magnetic resonance spectroscopy (1H MRS) as input data. Methods: Single-voxel stimulated echo acquisition mode (STEAM) (echo time of 20 ms) spectra were acquired from 138 subjects including 15 with low-grade gliomas, 47 with high-grade gliomas, 18 with tuberculomas, 18 with abscesses and 40 healthy controls. Two neural networks were constructed using the spectral points from 0.6 to 3.4 parts per million. In the first network construction, the ANN had to differentiate between tumours from infections, while the second network had to differentiate between all five histological classes. Results: ANN analysis gave a histologically correct diagnosis for low- and high-grade gliomas with an accuracy of 73% and 98% respectively. None of the 62 tumours was diagnosed as an infectious lesion. Among the non-neoplastic lesions, ANN classification was correct in 89% of tuberculomas and in 83% of brain abscesses. The specificity of ANN diagnosis was 98%, 92%, 99%, and 100% for low-grade gliomas, high-grade gliomas, tuberculomas and abscesses respectively. Conclusion: The present data show the clinical utility of non-invasive 1H MRS by automated ANN analysis in the diagnosis of tumour and non-tumour cerebral disorders.

    AB - Purpose: Experiments were carried out to assess the potential of artificial neural network (ANN) analysis in the differential diagnosis of brain tumours (low- and high-grade gliomas) from non-neoplastic focal brain lesions (tuberculomas and abscesses), using proton magnetic resonance spectroscopy (1H MRS) as input data. Methods: Single-voxel stimulated echo acquisition mode (STEAM) (echo time of 20 ms) spectra were acquired from 138 subjects including 15 with low-grade gliomas, 47 with high-grade gliomas, 18 with tuberculomas, 18 with abscesses and 40 healthy controls. Two neural networks were constructed using the spectral points from 0.6 to 3.4 parts per million. In the first network construction, the ANN had to differentiate between tumours from infections, while the second network had to differentiate between all five histological classes. Results: ANN analysis gave a histologically correct diagnosis for low- and high-grade gliomas with an accuracy of 73% and 98% respectively. None of the 62 tumours was diagnosed as an infectious lesion. Among the non-neoplastic lesions, ANN classification was correct in 89% of tuberculomas and in 83% of brain abscesses. The specificity of ANN diagnosis was 98%, 92%, 99%, and 100% for low-grade gliomas, high-grade gliomas, tuberculomas and abscesses respectively. Conclusion: The present data show the clinical utility of non-invasive 1H MRS by automated ANN analysis in the diagnosis of tumour and non-tumour cerebral disorders.

    U2 - 10.1007/s004320050284

    DO - 10.1007/s004320050284

    M3 - Article

    VL - 125

    SP - 343

    EP - 349

    JO - Journal of Cancer Research and Clinical Oncology

    JF - Journal of Cancer Research and Clinical Oncology

    SN - 0171-5216

    IS - 6

    ER -