Abstract
Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image features with random forest regressor approach was 51.5% for the training and 51.7% for the validation dataset. The gradient boosting regressor with shape features gave an accuracy of 91.5% and 62.1% on training and validation datasets respectively. It is better than the BraTS 2020 survival prediction challenge winners on the training and validation datasets. Our work shows that handcrafted features exhibit a strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.
All authors have contributed equally to this work.
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
Taylor, O.G., Brzozowski, J.S., Skelding, K.A.: Glioblastoma multiforme: an overview of emerging therapeutic targets. Front. Oncol. 9, 963 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241. Springer (2015)
Zhao, Y.X., Zhang, Y.M., Liu, C.L.: Bag of tricks for 3D MRI brain tumor segmentation. In: International MICCAI Brainlesion Workshop, pp. 210–220. Springer (2019)
McKinley, R., Rebsamen, M., Meier, R., Wiest, R.: Triplanar ensemble of 3D-to-2D CNNs with label-uncertainty for brain tumor segmentation. In: International MICCAI Brainlesion Workshop, pp. 379–387. Springer (2019)
Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded U-net: 1st place solution to brats challenge 2019 segmentation task. In: International MICCAI Brainlesion Workshop, pp. 231–241. Springer (2019)
Agravat, R.R., Raval, M.S.: Brain tumor segmentation and survival prediction. In: International MICCAI Brainlesion Workshop, pp. 338–348. Springer (2019)
Wang, S., Dai, C., Mo, Y., Angelini, E., Guo, Y., Bai, W.: Automatic brain tumour segmentation and biophysics-guided survival prediction. In: International MICCAI Brainlesion Workshop, pp. 61–72. Springer (2019)
Feng, X., Dou, Q., Tustison, N., Meyer, C.: Brain tumor segmentation with uncertainty estimation and overall survival prediction. In: International MICCAI Brainlesion Workshop, pp. 304–314. Springer (2019)
Wang, F., Jiang, R., Zheng, L., Meng, C., Biswal, B.: 3D U-net based brain tumor segmentation and survival days prediction. In: International MICCAI Brainlesion Workshop, pp. 131–141. Springer (2019)
Islam, M., Vibashan, V., Jose, V.J.M., Wijethilake, N., Utkarsh, U., Ren, H.: Brain tumor segmentation and survival prediction using 3D attention UNet. In: International MICCAI Brainlesion Workshop, pp. 262–272. Springer (2019)
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)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Agravat, R., Raval, M.S.: 3D semantic segmentation of brain tumor for overall survival prediction. arXiv preprint arXiv:2008.11576 (2020)
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge. In: International MICCAI Brainlesion Workshop, pp. 287–297. Springer (2017)
Van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Can. Res. 77(21), e104–e107 (2017)
He, B., Zhao, W., Pi, J.Y., Han, D., Jiang, Y.M., Zhang, Z.G.: A biomarker basing on radiomics for the prediction of overall survival in non-small cell lung cancer patients. Respir. Res. 19(1), 1–8 (2018)
Liu, C., et al.: Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J. Magn. Reson. Imaging 49(1), 131–140 (2019)
Li, Y., Jiang, J., Lu, J., Jiang, J., Zhang, H., Zuo, C.: Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG pet imaging and its implementation for Alzheimer’s disease and mild cognitive impairment. Ther. Adv. Neurol. Disord. 12, 1756286419838682 (2019)
Weninger, L., Haarburger, C., Merhof, D.: Robustness of radiomics for survival prediction of brain tumor patients depending on resection status. Front. Comput. Neurosci. 13, 73 (2019)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Lao, J., et al.: A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci. Rep. 7(1), 1–8 (2017)
Chaddad, A., Desrosiers, C., Hassan, L., Tanougast, C.: A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome. Br. J. Radiol. 89(1068), 20160575 (2016)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rajput, S., Agravat, R., Roy, M., Raval, M.S. (2022). Glioblastoma Multiforme Patient Survival Prediction. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_6
Download citation
DOI: https://doi.org/10.1007/978-981-16-3880-0_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3879-4
Online ISBN: 978-981-16-3880-0
eBook Packages: EngineeringEngineering (R0)