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Journal of Neuro-Oncology

, Volume 139, Issue 2, pp 491–499 | Cite as

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

  • Céline De Looze
  • Alan Beausang
  • Jane Cryan
  • Teresa Loftus
  • Patrick G. Buckley
  • Michael Farrell
  • Seamus Looby
  • Richard Reilly
  • Francesca Brett
  • Hugh Kearney
Clinical Study

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.

Keywords

Diagnostic accuracy Machine learning Glioma Random forest MRI 

Notes

Compliance with ethical standards

Conflict of interest

None of the authors of this study have any conflict of interest in relation to this work.

Human and animal participants

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.

Informed consent

Informed consent was obtained from all participants in this study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Céline De Looze
    • 1
    • 2
  • Alan Beausang
    • 3
  • Jane Cryan
    • 3
  • Teresa Loftus
    • 4
  • Patrick G. Buckley
    • 4
    • 5
  • Michael Farrell
    • 3
  • Seamus Looby
    • 6
  • Richard Reilly
    • 1
    • 7
    • 8
  • Francesca Brett
    • 3
  • Hugh Kearney
    • 3
  1. 1.Trinity Centre for BioengineeringTrinity College DublinDublinIreland
  2. 2.School of EngineeringTrinity College DublinDublinIreland
  3. 3.Department of NeuropathologyBeaumont HospitalDublinIreland
  4. 4.Department of Molecular PathologyBeaumont HospitalDublinIreland
  5. 5.Genomics Medicine IrelandDublinIreland
  6. 6.Department of NeuroradiologyBeaumont HospitalDublinIreland
  7. 7.Institute of NeurosciencesTrinity College DublinDublinIreland
  8. 8.School of MedicineTrinity College DublinDublinIreland

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