Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Jun;43(6):2835-2844.
doi: 10.1118/1.4948668.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

Panagiotis Korfiatis  1 , Timothy L Kline  1 , Lucie Coufalova  2 , Daniel H Lachance  3 , Ian F Parney  4 , Rickey E Carter  5 , Jan C Buckner  6 , Bradley J Erickson  1
Affiliations

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

Panagiotis Korfiatis et al. Med Phys. 2016 Jun.

Abstract

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O(6)-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes.

Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status.

Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78-0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813.

Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

PubMed Disclaimer

Figures

FIG. 1.
FIG. 1.
Diagram capturing dataset formation for this study.
FIG. 2.
FIG. 2.
Glioblastoma cases with methylated MGMT GBM tumor. The T1 postcontrast [(a), (d), and (g)], the T2 [(b), (e), and (h)], and the tumor ROI overlaid (blue) on the T1 postcontrast are depicted [(c), (f), and (i)].
FIG. 3.
FIG. 3.
Glioblastoma cases with unmethylated MGMT GBM tumor. The T1 postcontrast [(a), (d), and (g)], the T2 [(b), (e), and (h)], and the tumor ROI overlaid (blue) on the T1 postcontrast are depicted [(c), (f), and (i)].
FIG. 4.
FIG. 4.
Correlations of the selected features for the best-performing classification schemes.
FIG. 5.
FIG. 5.
Glioblastoma cases with methylated MGMT GBM tumor. Texture features corresponding to the best-performing classification scheme (Az = 0.850), cluster prominence [(b), (g), and (l)], correlation [(c), (h), and (m)], Haralick correlation [(d), (i), and (n)], inertia [(e), (j), and (o)], and corresponding tumor ROI [(a), (f), and (k)].
FIG. 6.
FIG. 6.
Glioblastoma cases with unmethylated MGMT GBM tumor. Texture features corresponding to the best-performing classification scheme (Az = 0.850), cluster prominence [(b), (g), and (l)], correlation [(c), (h), and (m)], Haralick correlation [(d), (i), and (n)], inertia [(e), (j), and (o)], and corresponding tumor ROI [(a), (f), and (k)].
See this image and copyright information in PMC

References

    1. Johnson D. R. and O’Neill B. P., “Glioblastoma survival in the United States before and during the temozolomide era,” J. Neuro-Oncol. 107, 359–364 (2012). 10.1007/s11060-011-0749-4 - DOI - PubMed
    1. Stupp R., Mason W. P., van den Bent M. J., Weller M., Fisher B., Taphoorn M. J., Belanger K., Brandes A. A., Marosi C., Bogdahn U., Curschmann J., Janzer R. C., Ludwin S. K., Gorlia T., Allgeier A., Lacombe D., Cairncross J. G., Eisenhauer E., Mirimanoff R. O., and European Organisation for Research and Treatment of Cancer, Brain Tumor, and Radiotherapy Groups, and National Cancer Institute of Canada Clinical Trials Group, “Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma,” N. Engl. J. Med. 352, 987–996 (2005). 10.1056/NEJMoa043330 - DOI - PubMed
    1. Ellingson B. M., Wen P. Y., van den Bent M. J., and Cloughesy T. F., “Pros and cons of current brain tumor imaging,” Neuro Oncol. 16(Suppl. 7), vii2–vii11 (2014). 10.1093/neuonc/nou224 - DOI - PMC - PubMed
    1. Rivera A. L., Pelloski C. E., Gilbert M. R., Colman H., De La Cruz C., Sulman E. P., Bekele B. N., and Aldape K. D., “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 (2010). 10.1093/neuonc/nop020 - DOI - PMC - PubMed
    1. Zhang K., Wang X. Q., Zhou B., and Zhang L., “The prognostic value of MGMT promoter methylation in glioblastoma multiforme: A meta-analysis,” Fam. Cancer 12, 449–458 (2013). 10.1007/s10689-013-9607-1 - DOI - PubMed

MeSH terms