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

Journal of Neuro-oncology
Céline De LoozeHugh Kearney

Abstract

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. 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. 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: AU...Continue Reading

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Citations

Mar 30, 2019·Critical Reviews in Clinical Laboratory Sciences·Christopher Naugler, Deirdre L Church
Dec 1, 2018·Japanese Journal of Radiology·Koji Sakai, Kei Yamada
Jun 14, 2021·Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia·Quinlan D BuchlakMassimo Piccardi
Aug 11, 2021·Neurologia Medico-chirurgica·Manabu KinoshitaHaruhiko Kishima
Jul 15, 2020·Academic Radiology·Sonal GoreMadhura Ingalhalikar

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