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.
bioRxiv Subject Collection: Neuroscience