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 Mickey; Toral R Patel; Baowei Fei; Ananth J Madhuranthakam; Joseph A Maldjian

doi:10.1093/noajnl/vdaa066

A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas

Neurooncol Adv. 2020 Jul 17;2(1):vdaa066. doi: 10.1093/noajnl/vdaa066. eCollection 2020 Jan-Dec.

Affiliations

¹ Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
² Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
³ Department of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA.

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 (T2w) MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network.

Methods: Multiparametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. 1p/19 co-deletions were present in 130 subjects. Two-hundred and thirty-eight 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. Receiver operating characteristic 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%, SD = 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 area under the curve 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 T2w MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

Keywords: 1p/19 co-deletion; deep learning; glioma.

Grants and funding

U01 CA207091/CA/NCI NIH HHS/United States