Abstract
Gliomas are common primary brain malignancies. The sub-regions of gliomas are depicted by MRI scans, reflecting varying biological properties. These properties have effect on the diagnosis of neurosurgeons on whether or what kind of resection should be done. The survival days after gross total resection is also of great concern. In this paper, we propose a semi-auto method for segmentation, and extract features from slices of MRI scans, including conventional MRI features and clinical features. 13 features of a subject are selected finally and a support vector regression is used to fit with the training data.
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
Akkus, Z., et al.: Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence. J. Digit. Imaging 30(4), 469–476 (2017). https://doi.org/10.1007/s10278-017-9984-3
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al.: 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
Carrillo, J., Lai, A., Nghiemphu, P., et al.: Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. Am. J. Neuroradiol. 33, 1349–1355 (2012)
Pan, C.-C., et al.: A machine learning-based prediction model of H3K27M mutations in brainstem gliomas using conventional MRI and clinical features. Radiother. Oncol. 130, 172–179 (2018). https://doi.org/10.1016/j.radonc.2018.07.011
Feng, X., Tustison, N., Meyer, C.: Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 279–288. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_25
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. arXiv preprint arXiv:1505.04597 (2015)
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4 (2017). Article number: 170117. https://doi.org/10.1038/sdata.2017.117
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, 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)
Zhou, C., et al.: Segmentation of peritumoral oedema offers a valuable radiological feature of cerebral metastasis. Br. J. Radiol. (2016). https://doi.org/10.1259/bjr.20151054
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ren, Y., Sun, P., Lu, W. (2020). Overall Survival Prediction Using Conventional MRI Features. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-46643-5_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46642-8
Online ISBN: 978-3-030-46643-5
eBook Packages: Computer ScienceComputer Science (R0)
