Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

• Authors: Peter Chang, University of California San Francisco; J. Grinband, Columbia University; Brent Weinberg, Emory University; M. Bardis, University of California Irvine; M. Khy, University of California Irvine; G. Cadena, University of California Irvine; M.Y. Su, University of California Irvine; S. Cha, University of California San Francisco; Christopher G. Filippi, North Shore University Hospital; D. Bota, University of California Irvine; Pierre Baldi, University of California Irvine; L.M. Poisson, Henry Ford Health System; R. Jain, New York University; Daniel Chow, University of California Irvine
• Journal Title: AMERICAN JOURNAL OF NEURORADIOLOGY
• Volume: Volume 39, Number 7
• Publisher: AMER SOC NEURORADIOLOGY | 2018-07-01, Pages 1201-1207
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3174/ajnr.A5667
• Subjects: Biology, Neuroscience; Health Sciences, Radiology
• Keywords: Science & Technology; Life Sciences & Biomedicine; Clinical Neurology; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging; Neurosciences & Neurology; MGMT PROMOTER METHYLATION; HIGH-GRADE GLIOMAS; PULMONARY TUBERCULOSIS; GLIOBLASTOMA; OLIGODENDROGLIOMA; CLASSIFICATION; OPTIMIZATION; REGISTRATION; SURVIVAL; ROBUST

Abstract

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.