Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.15.204933v1?rss=1 Authors: Bangalore Yogananda, C. G., Shah, B. R., Yu, F. F., Pinho, M. C., Nalawade, S. S., Murugesan, G. K., Wagner, B. C., Mickey, B., Patel, T., Fei, B., Madhuranthakam, A. J., Maldjian, J. A. Abstract: 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. Three-fold 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. Copy rights belong to original authors. Visit the link for more info