TY - JOUR T1 - MRI-Based Deep-Learning Method for Determining Glioma <em>MGMT</em> Promoter Methylation Status JF - American Journal of Neuroradiology JO - Am. J. Neuroradiol. SP - 845 LP - 852 DO - 10.3174/ajnr.A7029 VL - 42 IS - 5 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 Y1 - 2021/05/01 UR - http://www.ajnr.org/content/42/5/845.abstract N2 - Editorial expression of concern:In the May 2021 edition, the American Journal of Neuroradilogy published the article “MRI-Based Deep-Learning Method for Determining Glioma MGMT Promoter Methylation Status” by Yogananda CGB, et al.1 On August 22, 2022, the authors self-reported data errors related to the computer code and the training and testing data sets. The authors are now in the process of re-evaluating the accuracies using the correct test set. This notice of concern is to inform readers about these possible issues related to this articles results. After additional tests from the authors on the correct dataset are available, we will determine what additional action is warranted, such as an erratum.1. Yogananda CGB, Shah BR, Nalawade SS, et al. MRI-based deep-learning method for determining glioma MGMT promoter methylation status. American Journal of Neuroradilogy. 2021;42(5):845-852. doi:10.3174/AJNR.A7029BACKGROUND 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 ER -