Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study
To build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM).
In this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors.
The radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis.
Radiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features.
• Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts.
• All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features.
• Combing clinical factors with radiomics features did not benefit the prediction performance.
KeywordsRadiomics MGMT methylation Imaging biomarker Glioblastoma multiforme Imaging genomics
Area under the ROC curve
Fluid-attenuated inversion recovery
Grey-level co-occurrence matrix
Grey-level run length matrix
Grey level size zone matrix
Karnofsky performance score
Magnetic resonance imaging
Neighbourhood grey-tone difference matrix
Receiver operating characteristic curve
The Cancer Genome Atlas
The Cancer Imaging Archive
Visually Accusable Rembrandt Images
This study received funding by National Natural Science Foundation of China (No. 61571432), National Basic Research Program of China (973 Program, No. 2015CB755500), and Shenzhen Basic Research Program (JCYJ20170413162354654).
Compliance with ethical standards
The scientific guarantor of this publication is Hairong Zheng.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
One of the authors (Zhi-Cheng Li) has significant statistical expertise.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained from the three local institutions. Institutional Review Board approval for the TCIA data was not required.
• Diagnostic or prognostic study
• Multicentre study
- 1.Ostrom QT, Gittleman H, Xu J, et al (2016) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2009–2013. Neuro Oncol 18:v1–v75Google Scholar
- 15.VASARI Research Project, Available via https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project. Accessed 2 June 2017
- 18.Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by non-invasive imaging using a quantitative radiomics approach. Nat Comm 5Google Scholar
- 25.Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P (2017) Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. Eur Radiol 27:4188–4197CrossRefPubMedGoogle Scholar
- 34.Abadi M, Agarwal A, Barham P et al (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint:1603.04467Google Scholar
- 37.DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics:837-845Google Scholar
- 38.Nilsson R, Peña JM, Björkegren J, Tegnér J (2007) Consistent feature selection for pattern recognition in polynomial time. J Mach Learn Res 8:589–612Google Scholar
- 41.Baessler B, Mannil M, Oebel S, Maintz D, Alkadhi H, Manka R (2017) Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images. Radiology https://doi.org/10.1148/radiol.2017170213
- 43.Falconer DS, Mackay TFC (1996) Introduction to Genetics, Fourth edn. Addison Wesley Longman, Harlow, Essex, UKGoogle Scholar