Abstract
In glioblastoma (GBM), promoter methylation of the DNA repair gene MGMT is associated with benefit from chemotherapy. Because MGMT promoter methylation status can not be determined in all cases, a surrogate for the methylation status would be a useful clinical tool. Correlation between methylation status and magnetic resonance imaging features has been reported suggesting that non-invasive MGMT promoter methylation status detection is possible. In this work, a retrospective analysis of T2, FLAIR and T1-post contrast MR images in patients with newly diagnosed GBM is performed using L1-regularized neural networks. Tumor texture, assessed quantitatively was utilized for predicting the MGMT promoter methylation status of a GBM in 59 patients. The texture features were extracted using a space-frequency texture analysis based on the S-transform and utilized by a neural network to predict the methylation status of a GBM. Blinded classification of MGMT promoter methylation status reached an average accuracy of 87.7%, indicating that the proposed technique is accurate enough for clinical use.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Stupp, R., Mason, W., van den Bent, M., Weller, M., Fisher, B., Taphoorn, M., Belanger, K., Brandes, A., Bogdahn, C.M.U., Curschmann, J., Janzer, R., Ludwin, S., Gorlia, T., Allgeier, A., Lacombe, D., Cairncross, J., Eisenhauer, E., Mirimanoff, R.: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. New England Journal of Medicine 352(10), 987–996 (2005)
Peter, H., Roger, S.: MGMT methylation status: the advent of stratified therapy in glioblastoma? Disease markers 23(1-2), 97–104 (2007)
Hegi, M., Diserens, A., Gorlia, T., Hamou, M., de Tribolet, N., et al.: MGMT gene silencing and benefit form temozolomide in glioblastoma. New England Journal of Medicine 352(10), 997–1003 (2005)
Eoli, M., Menghi, F., Bruzzone, M., Simone, T.D., Valletta, L., Pollo, B., Bissola, L., Silvani, A., Bianchessi, D., D’Incerti, L., Filippini, G., Broggi, G., Boiardi, A., Finocchiaro, G.: Methylation of o6-methylguanine dna methyltransferase and loss of heterozygosity on 19q and/or 17p are overlapping features of secondary glioblastomas with prolonged survival. Clinical Cancer Research 13(9), 2606–2613 (2007)
Drabycz, S., Stockwell, R.G., Mitchell, R.: Image texture characterization using the discrete orthonormal s-transform. Journal of Digital Imaging (2008), doi:10.1007/s10278-008-9138-8
Zhang, Y., Wells, J., Buist, R., Peeling, J., Yong, V.W., Mitchell, J.R.: A novel MRI texture analysis of demyelination and inflammation in relapsing-remitting experimental allergic encephalomyelitis. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 760–767. Springer, Heidelberg (2006)
Brown, R., Zlatescu, M., Sijben, A., Roldan, G., Easaw, J., Forsyth, P., Parney, I., Sevick, R., Yan, E., Demetrick, D., Schiff, D., Cairncross, G., Mitchell, R.: The use of magnetic resonance imaging to noninvasively detect genetic signatures in oligodendroglioma. Clinical Cancer Research 14, 2357–2362 (2008)
Drabycz, S.: Effcient S-Transform Techniques for Magnetic Resonance Imaging. PhD thesis, University of Calgary (2009)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillian College Pub. Co. (1994)
Williams, P.M.: Bayesian regularisation and pruning using a laplace prior. Neural Computation 7, 117–143 (1995)
McAuliffe, M.J., Lalonde, F.M., McGarry, D., Gandler, W., Csaky, K., Trus, B.L.: Medical image processing, analysis & visualization in clinical research. In: CBMS 2001: Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems, Washington, DC, USA, p. 381. IEEE Computer Society, Los Alamitos (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Levner, I., Drabycz, S., Roldan, G., De Robles, P., Cairncross, J.G., Mitchell, R. (2009). Predicting MGMT Methylation Status of Glioblastomas from MRI Texture. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04271-3_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-04271-3_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04270-6
Online ISBN: 978-3-642-04271-3
eBook Packages: Computer ScienceComputer Science (R0)