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.
ABBREVIATIONS:
- CNN
- convolutional neural network
- IDH
- isocitrate dehydrogenase
- MGMT
- O6-methylguanine-DNA methyltransferase
- VASARI
- Visually AcceSAble Rembrandt Images
Footnotes
Disclosures: Peter Chang—RELATED: Grant: National Institutes of Health (National Institute of Biomedical Imaging and Bioengineering) T32 Training Grant, T32EB001631*. Christopher G. Filippi—UNRELATED: Consultancy: KOL Philips Healthcare, Comments: part of a Key Opinion Leaders consortium in which I have advocated for CNNs in advanced imaging including neoplasms; Payment for Lectures Including Service on Speakers Bureaus: Visiting Professor, Comments: In a talk on advanced imaging of tumor, some of the preliminary work was included. Pierre Baldi—RELATED: Grant: National Institutes of Health*; UNRELATED: Royalties: MIT Press, Cambridge University Press, Wiley. Daniel Chow—RELATED: Grant: funding support from Canon Medical Systems USA. *Money paid to the institution.
This work was supported by Canon Medical Systems USA. The work of P.B. was supported, in part, by the following grants: Defense Advanced Research Projects Agency D17AP00002 and National Institutes of Health GM123558. The work of P.C. was in part supported by the National Institutes of Health training grant T32EB001631.
- © 2018 by American Journal of Neuroradiology
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