Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas
By Peter D. Chang, J. Grinband, B. Weinberg, M. Bardis, M. Khy, G. Cadena, M. Su, S. Cha, C. Filippi, D. Bota and 4 more. American Journal of Neuroradiology, 2018
382 citations recorded by the source corpus.
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 IDH1 mutation status, 1p/19q codeletion, and MGMT promotor methylation status. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. The authors conclude that this shows the feasibility of a deep-learning CNN approach for the accurate classification of individual genetic mutations of both low- and high-grade gliomas and that the relevant MR imaging features acquired from an added dimensionality-reduction technique are concordant with existing literature, showing that neural networks are capable of learning key imaging components without prior feature selection or human directed training. 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.
Subfields: Genetics, Neurology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/4b42af5998d2a5145665285c457ce5280581bc17
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Citations in the corpus
- Machine Learning Methods in Drug Discovery (2020, methodology): “Combinations of these DL techniques, such as CNNs, have also been very successful in identifying gene mutations and disease targets [91,92].”
- Deep Learning Localizes and Identifies Polyps in Real Time with 96% Accuracy in Screening Colonoscopy (2018, background): “This is consistent with hundreds of experiments reported in the literature on “transfer learning” and our extensive experience with other biomedical imaging problems 30,31,32 .”
- How Machine Learning will Transform Biomedicine (2020, neutral): “…and other tissue samples (Way and Greene, 2019; Way et al., 2018), predicting genomic features of brain cancers using magnetic resonance images (Chang et al., 2018a), and forecasting cancer patient outcomes based on multi-omics (Chaudhary et al., 2018) or imaging-omics integrations…”
- A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images (2020, neutral): “The FC layer [50] is used as a classifier that makes a decision on the basis of features obtained from the convolutional and pooling layers.”