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01-09-2018 | Clinical Study

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

Authors: Céline De Looze, Alan Beausang, Jane Cryan, Teresa Loftus, Patrick G. Buckley, Michael Farrell, Seamus Looby, Richard Reilly, Francesca Brett, Hugh Kearney

Published in: Journal of Neuro-Oncology | Issue 2/2018

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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Title: Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma’s grade and IDH status
Authors: Céline De Looze
Alan Beausang
Jane Cryan
Teresa Loftus
Patrick G. Buckley
Michael Farrell
Seamus Looby
Richard Reilly
Francesca Brett
Hugh Kearney
Publication date: 01-09-2018
Publisher: Springer US
Published in: Journal of Neuro-Oncology / Issue 2/2018
Print ISSN: 0167-594X
Electronic ISSN: 1573-7373
DOI: https://doi.org/10.1007/s11060-018-2895-4

At a glance: The STEP trials

Springer Medicine

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

Abstract

Introduction

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Introduction

Methods

Results

Conclusions

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