Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images.

Journal Article (Journal Article)

PURPOSE: To employ deep learning to predict genomic subtypes of lower-grade glioma (LLG) tumors based on their appearance at MRI. MATERIALS AND METHODS: Imaging data from The Cancer Imaging Archive and genomic data from The Cancer Genome Atlas from 110 patients from five institutions with lower-grade gliomas (World Health Organization grade II and III) were used in this study. A convolutional neural network was trained to predict tumor genomic subtype based on the MRI of the tumor. Two different deep learning approaches were tested: training from random initialization and transfer learning. Deep learning models were pretrained on glioblastoma MRI, instead of natural images, to determine if performance was improved for the detection of LGGs. The models were evaluated using area under the receiver operating characteristic curve (AUC) with cross-validation. Imaging data and annotations used in this study are publicly available. RESULTS: The best performing model was based on transfer learning from glioblastoma MRI. It achieved AUC of 0.730 (95% confidence interval [CI]: 0.605, 0.844) for discriminating cluster-of-clusters 2 from others. For the same task, a network trained from scratch achieved an AUC of 0.680 (95% CI: 0.538, 0.811), whereas a model pretrained on natural images achieved an AUC of 0.640 (95% CI: 0.521, 0.763). CONCLUSION: These findings show the potential of utilizing deep learning to identify relationships between cancer imaging and cancer genomics in LGGs. However, more accurate models are needed to justify clinical use of such tools, which might be obtained using substantially larger training datasets.Supplemental material is available for this article.© RSNA, 2020.

Full Text

Duke Authors

Cited Authors

  • Buda, M; AlBadawy, EA; Saha, A; Mazurowski, MA

Published Date

  • January 2020

Published In

Volume / Issue

  • 2 / 1

Start / End Page

  • e180050 -

PubMed ID

  • 33937809

Pubmed Central ID

  • PMC8017403

Electronic International Standard Serial Number (EISSN)

  • 2638-6100

Digital Object Identifier (DOI)

  • 10.1148/ryai.2019180050

Language

  • eng

Conference Location

  • United States