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Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma’s grade and IDH status

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Abstract

Introduction

Machine learning methods have been introduced as a computer aided diagnostic tool, with applications to glioma characterisation on MRI. Such an algorithmic approach may provide a useful adjunct for a rapid and accurate diagnosis of a glioma. The aim of this study is to devise a machine learning algorithm that may be used by radiologists in routine practice to aid diagnosis of both: WHO grade and IDH mutation status in de novo gliomas.

Methods

To evaluate the status quo, we interrogated the accuracy of neuroradiology reports in relation to WHO grade: grade II 96.49% (95% confidence intervals [CI] 0.88, 0.99); III 36.51% (95% CI 0.24, 0.50); IV 72.9% (95% CI 0.67, 0.78). We derived five MRI parameters from the same diagnostic brain scans, in under two minutes per case, and then supplied these data to a random forest algorithm.

Results

Machine learning resulted in a high level of accuracy in prediction of tumour grade: grade II/III; area under the receiver operating characteristic curve (AUC) = 98%, sensitivity = 0.82, specificity = 0.94; grade II/IV; AUC = 100%, sensitivity = 1.0, specificity = 1.0; grade III/IV; AUC = 97%, sensitivity = 0.83, specificity = 0.97. Furthermore, machine learning also facilitated the discrimination of IDH status: AUC of 88%, sensitivity = 0.81, specificity = 0.77.

Conclusions

These data demonstrate the ability of machine learning to accurately classify diffuse gliomas by both WHO grade and IDH status from routine MRI alone—without significant image processing, which may facilitate usage as a diagnostic adjunct in clinical practice.

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Authors and Affiliations

  1. Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland

    Céline De Looze & Richard Reilly

  2. School of Engineering, Trinity College Dublin, Dublin, Ireland

    Céline De Looze

  3. Department of Neuropathology, Beaumont Hospital, Dublin, Ireland

    Alan Beausang, Jane Cryan, Michael Farrell, Francesca Brett & Hugh Kearney

  4. Department of Molecular Pathology, Beaumont Hospital, Dublin, Ireland

    Teresa Loftus & Patrick G. Buckley

  5. Genomics Medicine Ireland, Dublin, Ireland

    Patrick G. Buckley

  6. Department of Neuroradiology, Beaumont Hospital, Dublin, Ireland

    Seamus Looby

  7. Institute of Neurosciences, Trinity College Dublin, Dublin, Ireland

    Richard Reilly

  8. School of Medicine, Trinity College Dublin, Dublin, Ireland

    Richard Reilly

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  1. Céline De Looze
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  2. Alan Beausang
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Correspondence to Hugh Kearney.

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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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De Looze, C., Beausang, A., Cryan, J. et al. Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma’s grade and IDH status. J Neurooncol 139, 491–499 (2018). https://doi.org/10.1007/s11060-018-2895-4

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