A novel fully automated mri-based deep learning method for classification of 1P/19Q co-deletion status in brain gliomas

Chandan Ganesh Bangalore Yogananda, Bhavya R. Shah, Frank F. Yu, Marco C. Pinho, Sahil S. Nalawade, Gowtham K. Murugesan, Benjamin C. Wagner, Bruce E Mickey, Toral R. Patel, Baowei Fei, Ananth J Madhuranthakam, Joseph A Maldjian

Research output: Contribution to journalArticlepeer-review


Background: One of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network. Methods: Multi-parametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). 1p/19 co-deletions were present in 130 subjects. 238 subjects were non co-deleted. A T2w image only network (1p/19q-net) was developed to perform 1p/19q co-deletion status classification and simultaneous single-label tumor segmentation using 3D-Dense-UNets. Threefold cross-validation was performed to generalize the network performance. ROC analysis was also performed. Dice-scores were computed to determine tumor segmentation accuracy. Results: 1p/19q-net demonstrated a mean cross validation accuracy of 93.46% across the 3 folds (93.4%, 94.35%, and 92.62%, standard dev=0.8) in predicting 1p/19q co-deletion status with a sensitivity and specificity of 0.90 ±0.003 and 0.95 ±0.01, respectively and a mean AUC of 0.95 ±0.01. The whole tumor segmentation mean Dice-score was 0.80 ± 0.007. Conclusion: We demonstrate high 1p/19q co-deletion classification accuracy using only T2-weighted MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Jul 17 2020


  • 1p/19 co-deletion
  • CNN
  • Deep Learning
  • Glioma

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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