Abstract
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. 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. 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]. 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.
Highlights
BACKGROUND AND PURPOSEO6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation confers an improved prognosis and treatment response in gliomas
We demonstrate high classification accuracy in predicting MGMT promoter methylation status using only T2 weighted Images (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
Summary
Multiparametric MR images of patients with brain gliomas were obtained from The Cancer Imaging Archive (TCIA) data base.[14,15] The genomic information was obtained from both The Cancer Genome Atlas (TCGA) and TCIA data bases.[14,16,17] Subject datasets were screened for the availability of preoperative MR images, T2WI, and known MGMT promoter status. Ground truth whole-tumor masks for methylated and unmethylated MGMT promoter type were labeled with 1’s and 2’s, respectively (Fig 1). Network Details Transfer learning for determination of MGMT promoter status was implemented using our previously trained 3D-IDH network.[20] The decoder part of the network was fine-tuned for a voxelwise dual-class segmentation of the whole tumor, with 1 and 2 representing methylated and unmethylated MGMT promoter types, respectively. MGMT-net outputs 2 segmentation volumes (V1 and V2), which are combined to generate the voxelwise prediction of methylated and unmethylated MGMT promoter tumor voxels, respectively. This MGMT promoter determination process is fully automated, and a tumor segmentation map is a natu- Training and Segmentation Times ral output of the voxelwise classification approach. The Dice score calculates the spatial overlap between the ground truth segmentation and the network segmentation
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