Abstract
This paper aims to predict colon cancer patients’ survival by using deep learning to extract prognostic biomarkers from haematoxylin- and eosin (HE)-stained tissue slides. A deep convolutional neural network is trained by transfer learning using 100,000 HE images achieving a nine-class accuracy \({>}97\%\). This model is then used to segment digital whole slide images from a cohort of patients from the Cancer Genome Atlas (TCGA). The classification map produced is then used to quantify tumour–stroma ratio and tumour-infiltrating lymphocytes regions. These are then evaluated for their prognostic value for overall survival (OS) in a multivariate Cox proportional hazard model.
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
Cancer Research UK. Bowel cancer statistics. https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/bowel-cancer. Accessed 14 Aug 2019
G.W. van Pelt et al., Scoring the tumor-stroma ratio in colon cancer: procedure and recommendations. Virchows Archiv 473(4), 405–412 (2018)
Oscar G.F. Geessink et al., Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer. Cell. Oncol. 42(3), 331–341 (2019)
J.N. Kather, N. Halama, A. Marx, 100,000 histological images of human colorectal cancer and healthy tissue. Apr. 2018. https://doi.org/10.5281/zenodo.1214456.. https://doi.org/10.5281/zenodo.1214456
K. He et al., Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
G. Huang et al., Densely connected convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
F.N. Iandola et al., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5 MB model size. arXiv:1602.07360 (2016)
C. Szegedy et al., Rethinking the inception architecture for computer vision, in Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, 2818–2826 (2016)
M. Shaban et al., Prognostic significance of automated score of tumor infiltrating lymphocytes in oral cancer (2018)
E.L. Kaplan, P. Meier, Nonparametric estimation from incomplete observations. J. Am. Stat. Associat. 53.282, 457–481 (1958)
D.R. Cox, Regression models and life-tables. J. Royal Statistical Soc. Ser. B (Methodological) 34.2 187–202 (1972)
L.V. Maaten, G. Hinton, Visualizing data using t- SNE. J. Mach. Learn. Res. 9, pp 2579–2605 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gedeon, R., Nagar, A.K., Naguib, R. (2020). Using Convolutional Neural Networks to Predict Colon Cancer Patients Survival. In: Nagar, A., Deep, K., Bansal, J., Das, K. (eds) Soft Computing for Problem Solving 2019 . Advances in Intelligent Systems and Computing, vol 1139. Springer, Singapore. https://doi.org/10.1007/978-981-15-3287-0_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-3287-0_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3286-3
Online ISBN: 978-981-15-3287-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)