RT Journal Article
SR Electronic
T1 Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1201
OP 1207
DO 10.3174/ajnr.A5667
VO 39
IS 7
A1 P. Chang
A1 J. Grinband
A1 B.D. Weinberg
A1 M. Bardis
A1 M. Khy
A1 G. Cadena
A1 M.-Y. Su
A1 S. Cha
A1 C.G. Filippi
A1 D. Bota
A1 P. Baldi
A1 L.M. Poisson
A1 R. Jain
A1 D. Chow
YR 2018
UL http://www.ajnr.org/content/39/7/1201.abstract
AB BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.MATERIALS AND METHODS: MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification.RESULTS: Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features.CONCLUSIONS: Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.CNNconvolutional neural networkIDHisocitrate dehydrogenaseMGMTO6-methylguanine-DNA methyltransferaseVASARIVisually AcceSAble Rembrandt Images