Abstract
Early diagnosis of brain tumor is crucial for treatment planning. Quantitative analyses of segmentation can provide information for tumor survival prediction. The effectiveness of convolutional neural network (CNN) has been validated in medical image segmentation. In this study, we apply a widely-employed CNN namely UNet to automatically segment out glioma sub-regions, and then extract their volumes and surface areas. A sophisticated machine learning scheme, consisting of mutual information feature selection and multivariate linear regression, is then used to predict individual survival time. The proposed method achieves an accuracy of 0.475 on 369 training data based on leave-one-out cross-validation. Compared with using all features, using features obtained from the employed feature selection technology can enhance the survival prediction performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Stupp, R., Mason, W.P., et al.: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352(10), 987–996 (2005)
Kumar, V., Gu, Y., Basu, S., Berglund, A., et al.: Radiomics: the process and the challenges. Magn. Reson. Imaging 30(9), 1234–1248 (2012)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BraTS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Bakas S., Reyes M., Jakab A., 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)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-428
Isensee, F., Jger, P. F., Kohl, S. A., et al.: Automated design of deep learning methods for biomedical image segmentation. arXiv preprint arXiv:1904.08128 (2020)
Sun, L., Zhang, S., Luo, L.: Tumor segmentation and survival prediction in Glioma with deep learning. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 83–93. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_8
Feng, X., Tustison, J., Patel, H., et al.: Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features. Front. Comput. Neurosci. 14, 25 (2020)
Li, J., Cheng, K., Wang, S., et al.: Feature selection: a data perspective. ACM Comput. Surv. (CSUR) 50(6), 1–45 (2017)
Kozachenko, L.F., Leonenko, N.N.: Sample estimate of the entropy of a random vector. Problemy Peredachi Informatsii 23(2), 9–16 (1987)
Kraskov, A., Stögbauer, H., Grassberger, P.: Estimating mutual information. Phys. Rev. E 69(6), 066138 (2004)
Ross, B.C.: Mutual information between discrete and continuous data sets. PloS one 9(2), e87357 (2014)
Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 234–244. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_21
Yang, H.-Y., Yang, J.: Automatic brain tumor segmentation with contour aware residual network and adversarial training. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 267–278. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_24
Gates, E., Pauloski, J.G., Schellingerhout, D., Fuentes, D.: Glioma segmentation and a simple accurate model for overall survival prediction. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 476–484. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_42
Acknowledgement
This work was supported by the Shenzhen Basic Research Program (JCYJ20190809120205578), the National Key R&D Program of China (2017YFC0112404), and the National Natural Science Foundation of China (81501546).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, J., Zhang, Y., Huang, W., Lin, L., Wang, K., Tang, X. (2021). Survival Prediction of Glioma Tumors Using Feature Selection and Linear Regression. In: Gao, W., et al. Intelligent Computing and Block Chain. FICC 2020. Communications in Computer and Information Science, vol 1385. Springer, Singapore. https://doi.org/10.1007/978-981-16-1160-5_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-1160-5_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1159-9
Online ISBN: 978-981-16-1160-5
eBook Packages: Computer ScienceComputer Science (R0)