Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.
J Neurooncol
; 139(2): 491-499, 2018 Sep.
Article
em En
| MEDLINE
| ID: mdl-29770897
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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Imageamento por Ressonância Magnética
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Aprendizado de Máquina
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Glioma
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Isocitrato Desidrogenase
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Mutação
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article