Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study

  • Zhi-Cheng Li
  • Hongmin Bai
  • Qiuchang Sun
  • Qihua Li
  • Lei Liu
  • Yan Zou
  • Yinsheng Chen
  • Chaofeng Liang
  • Hairong Zheng
Computer Applications
  • 130 Downloads

Abstract

Objectives

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).

Methods

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.

Results

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.

Conclusions

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.

Key Points

• 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.

Keywords

Radiomics MGMT methylation Imaging biomarker Glioblastoma multiforme Imaging genomics 

Abbreviations

AUC

Area under the ROC curve

FLAIR

Fluid-attenuated inversion recovery

GBM

Glioblastoma multiforme

GLCM

Grey-level co-occurrence matrix

GLRLM

Grey-level run length matrix

GLSZM

Grey level size zone matrix

KPS

Karnofsky performance score

MGMT

O6-methylguanine-DNA methyltransferase

MRI

Magnetic resonance imaging

NGTDM

Neighbourhood grey-tone difference matrix

ROC

Receiver operating characteristic curve

TCGA

The Cancer Genome Atlas

TCIA

The Cancer Imaging Archive

VASARI

Visually Accusable Rembrandt Images

Notes

Compliance with ethical standards

Guarantor

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.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained from the three local institutions. Institutional Review Board approval for the TCIA data was not required.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicentre study

References

  1. 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
  2. 2.
    Reardon DA, Wen PY (2015) Glioma in 2014: unravelling tumour heterogeneity-implications for therapy. Nat Rev Clin Oncol 12:69–70CrossRefPubMedGoogle Scholar
  3. 3.
    Weller M, Stupp R, Reifenberger G et al (2010) MGMT promoter methylation in malignant gliomas: ready for personalized medicine? Nat Rev Neurol 6:39–51CrossRefPubMedGoogle Scholar
  4. 4.
    Hegi ME, Diserens AC, Gorlia T et al (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997–1003CrossRefPubMedGoogle Scholar
  5. 5.
    Stupp R, Hegi ME, Mason WP et al (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10:459–466CrossRefPubMedGoogle Scholar
  6. 6.
    Sottoriva A, Spiteri I, Piccirillo SG et al (2013) Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci 110:4009–4014CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Patel AP, Tirosh I, Trombetta JJ et al (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344:1396–1401CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Dunn J, Baborie A, Alam F et al (2009) Extent of MGMT promoter methylation correlates with outcome in glioblastomas given temozolomide and radiotherapy. Brit J Cancer 101:124–131CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Parker NR, Hudson AL, Khong P et al (2016) Intratumoral heterogeneity identified at the epigenetic, genetic and transcriptional level in glioblastoma. Sci Rep 6:22477CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Drabycz S, Roldán G, De Robles P et al (2010) An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging. Neuroimage 49:1398–1405CrossRefPubMedGoogle Scholar
  11. 11.
    Carrillo JA, Lai A, Nghiemphu PL et al (2012) Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. Am J Neuroradiol 33:1349–1355CrossRefPubMedGoogle Scholar
  12. 12.
    Moon WJ, Choi JW, Roh HG, Lim SD, Koh YC (2012) Imaging parameters of high grade gliomas in relation to the MGMT promoter methylation status: the CT, diffusion tensor imaging, and perfusion MR imaging. Neuroradiology 54:555–563CrossRefPubMedGoogle Scholar
  13. 13.
    Korfiatis P, Kline TL, Coufalova L et al (2016) MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med Phys 43:2835–2844CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Kanas VG, Zacharaki EI, Thomas GA, Zinn PO, Megalooikonomou V, Colen RR (2017) Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Comput Meth Prog Bio 140:249–257CrossRefGoogle Scholar
  15. 15.
    VASARI Research Project, Available via https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project. Accessed 2 June 2017
  16. 16.
    Lambin P, Leijenaar RT, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762CrossRefPubMedGoogle Scholar
  17. 17.
    Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMedPubMedCentralGoogle Scholar
  18. 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
  19. 19.
    Itakura H, Achrol AS, Mitchell LA et al (2015) Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 7:303ra138–303ra138CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Cui Y, Tha KK, Terasaka S et al (2015) Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 278:546–553CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Li Q, Bai H, Chen Y et al (2017) A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme. Sci Rep 7:14331CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Kickingereder P, Götz M, Muschelli J et al (2016) Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Cui Y, Ren S, Tha KK, Wu J, Shirato H, Li R (2017) Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. Eur Radiol 27:3583–3592CrossRefPubMedGoogle Scholar
  24. 24.
    Yoon RG, Kim HS, Paik W, Shim WH, Kim SJ, Kim JH (2017) Different diagnostic values of imaging parameters to predict pseudoprogression in glioblastoma subgroups stratified by MGMT promoter methylation. Eur Radiol 27:255–266CrossRefPubMedGoogle Scholar
  25. 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
  26. 26.
    Vartanian A, Singh SK, Agnihotri S et al (2014) GBM's multifaceted landscape: highlighting regional and microenvironmental heterogeneity. Neuro-oncology 16:1167–1175CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269:8–14CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Lemée JM, Clavreul A, Menei P (2015) Intratumoral heterogeneity in glioblastoma: don't forget the peritumoral brain zone. Neuro-oncology 17:1322–1332CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Havik AB, Brandal P, Honne H et al (2012) MGMT promoter methylation in gliomas-assessment by pyrosequencing and quantitative methylation-specific PCR. J Transl Med 10:36CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE T Med Imaging 29:1310–1320CrossRefGoogle Scholar
  31. 31.
    Lötjönen JM, Wolz R, Koikkalainen JR et al (2010) Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage 49:2352–2365CrossRefPubMedGoogle Scholar
  32. 32.
    Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE T Med Imaging 35:1240–1251CrossRefGoogle Scholar
  33. 33.
    Menze BH, Jakab A, Bauer S et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE T Med Imaging 34:1993–2024CrossRefGoogle Scholar
  34. 34.
    Abadi M, Agarwal A, Barham P et al (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint:1603.04467Google Scholar
  35. 35.
    Kursa MB, Rudnicki WR (2010) Feature selection with the Boruta package. J Stat Softw 36:1–13CrossRefGoogle Scholar
  36. 36.
    Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  37. 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. 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
  39. 39.
    Kursa MB (2014) Robustness of Random Forest-based gene selection methods. BMC bioinformatics 15:8CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Guo P, Luo Y, Mai G et al (2014) Gene expression profile based classification models of psoriasis. Genomics 103:48–55CrossRefPubMedGoogle Scholar
  41. 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
  42. 42.
    Yamamoto S, Korn RL, Oklu R et al (2014) ALK molecular phenotype in non–small cell lung cancer: CT radiogenomic characterization. Radiology 272:568–576CrossRefPubMedGoogle Scholar
  43. 43.
    Falconer DS, Mackay TFC (1996) Introduction to Genetics, Fourth edn. Addison Wesley Longman, Harlow, Essex, UKGoogle Scholar
  44. 44.
    Burrell RA, McGranahan N, Bartek J, Swanton C (2013) The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501:338–345CrossRefPubMedGoogle Scholar
  45. 45.
    McCarthy N (2012) Tumour heterogeneity: Darwin's finches. Nat Rev Cancer 12:317–317PubMedGoogle Scholar
  46. 46.
    Polyak K (2014) Tumor heterogeneity confounds and illuminates: a case for Darwinian tumor evolution. Nat Med 20:344–346CrossRefPubMedGoogle Scholar
  47. 47.
    Reifenberger G, Hentschel B, Felsberg J et al (2012) Predictive impact of MGMT promoter methylation in glioblastoma of the elderly. Int J Cancer 131:1342–1350CrossRefPubMedGoogle Scholar
  48. 48.
    O'connor JP, Aboagye EO, Adams JE et al (2016) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.Department of NeurosurgeryGuangzhou General Hospital of Guangzhou Military CommandGuangzhouChina
  3. 3.Department of RadiologyThe 3rd Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  4. 4.Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangzhouChina
  5. 5.Department of NeurosurgeryThe 3rd Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina

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