Abstract
Patients with Glioblastoma multiforme (GBM) have a very low overall survival (OS) time, due to the rapid growth an invasiveness of this brain tumor. As a contribution to the overall survival (OS) prediction task within the Brain Tumor Segmentation Challenge (BraTS), we classify the OS of GBM patients into overall survival classes based on information derived from pre-treatment Magnetic Resonance Imaging (MRI). The top-ranked methods from the past years almost exclusively used shape and position features. This is a remarkable contrast to the current advances in GBM radiomics showing a benefit of intensity-based features. This discrepancy may be caused by the inconsistent acquisition parameters in a multi-center setting. In this contribution, we test if normalizing the images based on the healthy tissue intensities enables the robust use of intensity features in this challenge. Based on these normalized images, we test the performance of 176 combinations of feature selection techniques and classifiers. Additionally, we test the incorporation of a sequence and robustness prior to limit the performance drop when models are applied to unseen data. The most robust performance on the training data (accuracy: \(0.52\pm 0.09\)) was achieved with random forest regression, but this accuracy could not be maintained on the test set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)
Bae, S., et al.: Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology 289(3), 797–806 (2018). https://doi.org/10.1148/radiol.2018180200. http://pubs.rsna.org/doi/10.1148/radiol.2018180200
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. (2017). https://doi.org/10.1038/sdata.2017.117
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017). https://doi.org/10.1038/sdata.2017.117
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Ellingson, B.M., Wen, P.Y., Cloughesy, T.F.: Modified criteria for radiographic response assessment in glioblastoma clinical trials. Neurotherapeutics 14(2), 307–320 (2017). https://doi.org/10.1007/s13311-016-0507-6
van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104–e107 (2017). https://doi.org/10.1158/0008-5472.CAN-17-0339. https://cancerres.aacrjournals.org/content/77/21/e104
Hegi, M.E., et al.: MGMT gene silencing and benefit from temozolomide in glioblastoma. New Engl. J. Med. 352(10), 997–1003 (2005)
Isensee, F., Petersen, J., Kohl, S.A., Jäger, P.F., Maier-Hein, K.H.: nnU-Net: breaking the spell on successful medical image segmentation, vol. 1, pp. 1–8. arXiv preprint arXiv:1904.08128 (2019)
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62(2), 782–790 (2012)
Kickingereder, P., et al.: Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280(3), 880–889 (2016). https://doi.org/10.1148/radiol.2016160845. http://fsl.fmrib
Kickingereder, P., et al.: Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 20(5), 728–740 (2019). https://doi.org/10.1016/S1470-2045(19)30098-1
Lao, J., et al.: A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci. Rep. 7(1), 1–8 (2017). https://doi.org/10.1038/s41598-017-10649-8
Mazziotta, J., et al.: A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM). Philos. Trans. Roy. Soc. Lond. Ser. B: Biol. Sci. 356(1412), 1293–1322 (2001)
Menze, B.H., Jakab, A., Van Leemput, K.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). https://doi.org/10.1109/TMI.2014.2377694
Papanikolaou, N., Matos, C., Koh, D.M.: How to develop a meaningful radiomic signature for clinical use in oncologic patients. Cancer Imaging 20, 1–10 (2020). https://doi.org/10.1186/s40644-020-00311-4
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Reuter, M., Rosas, H.D., Fischl, B.: Highly accurate inverse consistent registration: a robust approach. NeuroImage 53(4), 1181–1196 (2010). https://doi.org/10.1016/J.NEUROIMAGE.2010.07.020
Saha, A., Yu, X., Sahoo, D., Mazurowski, M.A.: Effects of MRI scanner parameters on breast cancer radiomics. Expert Syst. Appl. 87, 384–391 (2017)
Stupp, R., et al.: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. New Engl. J. Med. 352(10), 987–996 (2005). https://doi.org/10.1056/NEJMoa043330
Suchorska, B., et al.: Complete resection of contrast-enhancing tumor volume is associated with improved survival in recurrent glioblastoma-results from the DIRECTOR trial. Neuro-Oncol. 18(4), 549–556 (2016). https://doi.org/10.1093/neuonc/nov326https://doi.org/10.1093/neuonc/nov326
Suter, Y., et al.: Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques. Cancer Imaging 20(1), 1–13 (2020)
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
Weller, M., et al.: European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas (2017). https://doi.org/10.1016/S1470-2045(17)30194-8
Weninger, L., Rippel, O., Koppers, S., Merhof, D.: Segmentation of brain tumors and patient survival prediction: methods for the BraTS 2018 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 3–12. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_1
Yeo, I., Johnson, R.A.: A new family of power transformations to improve normality or symmetry. Biometrika 87(4), 954–959 (2000). https://doi.org/10.1093/biomet/87.4.954
Acknowledgements
We gratefully acknowledge the funding received from the Swiss Cancer League (Krebsliga Schweiz), grant KFS-3979-08-2016 and the NVIDIA Corporation for donating a Titan Xp GPU. Computations were partly performed on Ubelix, the HCP cluster at the University of Bern.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Suter, Y., Knecht, U., Wiest, R., Reyes, M. (2021). Overall Survival Prediction for Glioblastoma on Pre-treatment MRI Using Robust Radiomics and Priors. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-72084-1_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72083-4
Online ISBN: 978-3-030-72084-1
eBook Packages: Computer ScienceComputer Science (R0)
