Work

MRI-Based Deep-Learning Method for Determining Glioma MGMT Promoter Methylation Status

Year: 2021

Type: article

Abstract: ABSTRACT PURPOSE Methylation of the O 6 -Methylguanine-DNA Methyltransferase (MGMT) promoter results in epigenetic silencing of the MGMT enzyme and confers an improved prognosis and treatment response in gliomas. The purpose of this study was to develop a deep-learning network for determining the methylation status of the MGMT Promoter in gliomas using T2-w magnetic resonance images only. METHODS Brain MRI and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). 163 subjects had a methylated MGMT promoter. A T2-w image 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 networks’ performance. Dice-scores were computed to determine tumor segmentation accuracy. RESULTS MGMT-net demonstrated a mean cross validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, standard dev=0.66) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% ±0.04 and 91.66% ±2.06, respectively and a mean AUC of 0.93 ±0.01. The whole tumor segmentation mean Dice-score was 0.82 ± 0.008. CONCLUSION We demonstrate high classification accuracy in predicting the methylation status of the MGMT promoter using only T2-w MR images that surpasses the sensitivity, specificity, and accuracy of invasive histological methods such as pyrosequencing, methylation-specific PCR, and immunofluorescence methods. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

Source: American Journal of Neuroradiology

DOI: 10.3174/ajnr.a7029