Journal of Digital Imaging

, Volume 30, Issue 5, pp 622–628 | Cite as

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status

  • Panagiotis Korfiatis
  • Timothy L. Kline
  • Daniel H. Lachance
  • Ian F. Parney
  • Jan C. Buckner
  • Bradley J. EricksonEmail author


Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/− 3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/− 13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/− 20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p < 0.001). We report a method that alleviates the need of extensive preprocessing and acts as a proof of concept that deep neural architectures can be used to predict molecular biomarkers from routine medical images.


MRI Deep learning MGMT methylation 



This study was supported by the NCI Grant CA160045, NVidia: Research Support.


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Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  • Panagiotis Korfiatis
    • 1
  • Timothy L. Kline
    • 1
  • Daniel H. Lachance
    • 2
  • Ian F. Parney
    • 3
  • Jan C. Buckner
    • 4
  • Bradley J. Erickson
    • 1
    Email author
  1. 1.Department of RadiologyMayo ClinicRochesterUSA
  2. 2.Department of NeurologyMayo ClinicRochesterUSA
  3. 3.Department of Neurologic SurgeryMayo ClinicRochesterUSA
  4. 4.Department of Medical OncologyMayo ClinicRochesterUSA

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