Advertisement

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
Article

Abstract

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.

Keywords

MRI Deep learning MGMT methylation 

Notes

Acknowledgements

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

References

  1. 1.
    Johnson DR, O’Neill BP: Glioblastoma survival in the United States before and during the temozolomide era. J Neurooncol 107:359–364, 2011CrossRefPubMedGoogle Scholar
  2. 2.
    Ellingson BM, Wen PY, van den Bent MJ, Cloughesy TF: Pros and cons of current brain tumor imaging. Neuro Oncol 16(Suppl 7):vii2–vi11, 2014CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Weizman L, Ben-Sira L, Joskowicz L, Aizenstein O, Shofty B, Constantini S, Ben-Bashat D: Prediction of brain MR scans in longitudinal tumor follow-up studies. Med Image Comput Comput Assist Interv 15:179–187, 2012PubMedGoogle Scholar
  4. 4.
    Law M, Young RJ, Babb JS, Peccerelli N, Chheang S, Gruber ML, Miller DC, Golfinos JG, Zagzag D, Johnson G: Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 247:490–498, 2008CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Jain R, Poisson LM, Gutman D et al.: Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology 272:484–493, 2014CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Zhang K, Wang X-Q, Zhou B, Zhang L: The prognostic value of MGMT promoter methylation in glioblastoma multiforme: a meta-analysis. Fam Cancer 12:449–458, 2013CrossRefPubMedGoogle Scholar
  7. 7.
    Li H, Li J, Cheng G, Zhang J, Li X: IDH mutation and MGMT promoter methylation are associated with the pseudoprogression and improved prognosis of glioblastoma multiforme patients who have undergone concurrent and adjuvant temozolomide-based chemoradiotherapy. Clin Neurol Neurosurg 151:31–36, 2016CrossRefPubMedGoogle Scholar
  8. 8.
    Rivera AL, Pelloski CE, Gilbert MR, Colman H, De La Cruz C, Sulman EP, Bekele BN, Aldape KD: MGMT promoter methylation is predictive of response to radiotherapy and prognostic in the absence of adjuvant alkylating chemotherapy for glioblastoma. Neuro Oncol 12:116–121, 2010CrossRefPubMedGoogle Scholar
  9. 9.
    Ellingson BM: Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics. Curr Neurol Neurosci Rep 15:506, 2015CrossRefPubMedGoogle Scholar
  10. 10.
    Rundle-Thiele D, Day B, Stringer B et al.: Using the apparent diffusion coefficient to identifying MGMT promoter methylation status early in glioblastoma: importance of analytical method. J Med Radiat Sci 62:92–98, 2015CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Drabycz S, Roldán G, de Robles P, Adler D, McIntyre JB, Magliocco AM, Cairncross JG, Mitchell JR: An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging. Neuroimage 49:1398–1405, 2010CrossRefPubMedGoogle Scholar
  12. 12.
    Levner I, Drabycz S, Roldan G, De Robles P, Gregory Cairncross J, Mitchell R: Predicting MGMT Methylation Status of Glioblastomas from MRI Texture. Med Image Comput Comput Assist Interv. 2009;12(Pt 2):522–530Google Scholar
  13. 13.
    Moon W-J, Choi JW, Roh HG, Lim SD, Koh Y-C: Imaging parameters of high grade gliomas in relation to the MGMT promoter methylation status: the CT, diffusion tensor imaging, and perfusion MR imaging. Neuroradiology 54:555–563, 2012CrossRefPubMedGoogle Scholar
  14. 14.
    Ahn SS, Shin N-Y, Chang JH, Kim SH, Kim EH, Kim DW, Lee S-K: Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging. J Neurosurg 121:367–373, 2014CrossRefPubMedGoogle Scholar
  15. 15.
    Gupta A, Omuro AMP, Shah AD, Graber JJ, Shi W, Zhang Z, Young RJ: Continuing the search for MR imaging biomarkers for MGMT promoter methylation status: conventional and perfusion MRI revisited. Neuroradiology 54:641–643, 2012CrossRefPubMedGoogle Scholar
  16. 16.
    Kanas VG, Zacharaki EI, Thomas GA, Zinn PO, Megalooikonomou V, Colen RR: Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Comput Methods Programs Biomed 140:249–257, 2017CrossRefPubMedGoogle Scholar
  17. 17.
    Korfiatis P, Kline TL, Coufalova L, Lachance DH, Parney IF, Carter RE, Buckner JC, Erickson BJ: MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med Phys 43:2835, 2016CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Eckel-Passow JE, Lachance DH, Molinaro AM et al.: Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508, 2015CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Greenspan H, van Ginneken B, Summers RM: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35:1153–1159, 2016CrossRefGoogle Scholar
  20. 20.
    Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35:1207–1216, 2016CrossRefPubMedGoogle Scholar
  21. 21.
    Dalmış MU, Litjens G, Holland K, Setio A, Mann R, Karssemeijer N, Gubern-Mérida A: Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Med Phys 44:533–546, 2017CrossRefPubMedGoogle Scholar
  22. 22.
    Dhungel N, Carneiro G, Bradley AP: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 37:114–128, 2017CrossRefPubMedGoogle Scholar
  23. 23.
    Spampinato C, Palazzo S, Giordano D, Aldinucci M, Leonardi R: Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal 36:41–51, 2017CrossRefPubMedGoogle Scholar
  24. 24.
    Yan Z, Zhan Y, Zhang S, Metaxas D, Zhou XS: Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition. IEEE Transactions On Medical Imaging. doi: 10.1109/TMI.2016.2524985
  25. 25.
    Rajkomar A, Lingam S, Taylor AG, Blum M, Mongan J: High-throughput classification of radiographs using deep convolutional neural networks. J Digit Imaging 30:95–101, 2017CrossRefPubMedGoogle Scholar
  26. 26.
    Korfiatis PD, Kline TL, Blezek DJ, Langer SG, Ryan WJ, Erickson BJ: MIRMAID: a content management system for medical image analysis research. Radiographics 35:1461–1468, 2015CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC: N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320, 2010CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Juntu J, Sijbers J, Dyck D, Gielen J: Bias Field Correction for MRI Images. In: Advances in Soft Computing. Springer. pp 543–551Google Scholar
  29. 29.
    He K, Zhang X, Ren S, Sun J: Deep Residual Learning for Image Recognition. arXiv [cs.CV]. 2015. https://arxiv.org/abs/1512.03385
  30. 30.
    He K, Zhang X, Ren S, Sun J: Identity Mappings in Deep Residual Networks. In: Lecture Notes in Computer Science. 2016, pp 630–645. https://link.springer.com/chapter/10.1007/978-3-319-46493-0_38
  31. 31.
    He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas. 2016, pp 770–778Google Scholar
  32. 32.
    He K, Zhang X, Ren S, Sun J: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 I.E. International Conference on Computer Vision (ICCV), 2015. doi:  10.1109/iccv.2015.123
  33. 33.
    loffe S, Szegedy C: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv [cs.LG]. 2015. https://arxiv.org/abs/1502.03167
  34. 34.
    Dietterich TG: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10:1895–1923, 1998 1998CrossRefPubMedGoogle Scholar
  35. 35.
    Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, Vol. 8 (1936), pp. 3–62 Key: citeulike:1778138Google Scholar
  36. 36.
    Veit A, Wilber M, Belongie S: Residual Networks Behave Like Ensembles of Relatively Shallow Networks. arXiv [cs.CV]. 2016. https://arxiv.org/abs/1605.06431
  37. 37.
    Nyúl LG, Udupa JK: On standardizing the MR image intensity scale. Magn Reson Med 42:1072–1081, 1999CrossRefPubMedGoogle Scholar

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

Personalised recommendations