PT  - JOURNAL ARTICLE
AU  - C.G.B. Yogananda
AU  - B.R. Shah
AU  - S.S. Nalawade
AU  - G.K. Murugesan
AU  - F.F. Yu
AU  - M.C. Pinho
AU  - B.C. Wagner
AU  - B. Mickey
AU  - T.R. Patel
AU  - B. Fei
AU  - A.J. Madhuranthakam
AU  - J.A. Maldjian
TI  - MRI-Based Deep-Learning Method for Determining Glioma <em>MGMT</em> Promoter Methylation Status
AID  - 10.3174/ajnr.A7029
DP  - 2021 Mar 04
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2021/03/04/ajnr.A7029.short
4100  - http://www.ajnr.org/content/early/2021/03/04/ajnr.A7029.full
AB  - BACKGROUND AND PURPOSE: O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining MGMT promoter methylation status using T2 weighted Images (T2WI) only.MATERIALS AND METHODS: Brain MR imaging and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. One hundred sixty-three subjects had a methylated MGMT promoter. A T2WI-only network (MGMT-net) was developed to determine MGMT promoter methylation status and simultaneous single-label tumor segmentation. The network was trained using 3D-dense-UNets. Three-fold cross-validation was performed to generalize the performance of the networks. Dice scores were computed to determine tumor-segmentation accuracy.RESULTS: The MGMT-net demonstrated a mean cross-validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, [SD, 0.66%]) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% [SD, 0.04%] and 91.66% [SD, 2.06%], respectively, and a mean area under the curve of 0.93 [SD, 0.01]. The whole tumor-segmentation mean Dice score was 0.82 [SD, 0.008].CONCLUSIONS: We demonstrate high classification accuracy in predicting MGMT promoter methylation status using only T2WI. Our network surpasses the sensitivity, specificity, and accuracy of histologic and molecular methods. This result represents an important milestone toward using MR imaging to predict prognosis and treatment response.IDHisocitrate dehydrogenaseMGMTO6-methylguanine-DNA methyltransferasePCRpolymerase chain reactionT2WIT2 weighted ImagesTCGAThe Cancer Genome AtlasTCIAThe Cancer Imaging Archive