Abstract
Survival prediction for patients based on gigapixel histopathological whole-slide images (WSIs) has attracted increasing attention in recent years. Previous studies mainly focus on the framework of predicting the survival hazard scores based on one individual WSI for each patient directly. These prediction methods ignore the relative survival differences among patients, i.e., the ranking information, which is important for a regression task. Under such circumstances, we propose a ranking-based survival prediction method on WSIs – RankSurv, which takes the ranking information into consideration during the learning process. First, a hypergraph representation is introduced to conduct hazard prediction on each WSI respectively, which is able to learn the high-order correlation among different patches in the WSI. Then, a ranking-based prediction process is conducted using pairwise survival data. Experiments are conducted on three public carcinoma datasets (i.e., LUSC, GBM, and NLST). Quantitative results show that the proposed method significantly outperforms state-of-the-art methods on all three datasets, which demonstrates the effectiveness of the proposed ranking-based survival prediction framework.
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
Burges, C.J.: From ranknet to lambdarank to lambdaMART: an overview. Learning 11(23–581), 81 (2010)
Bychkov, D., et al.: Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep. 8(1), 1–11 (2018)
Cox, D.R.: Regression models and life-tables. J. Roy. Stat. Soc.: Ser. B (Methodol.) 34(2), 187–202 (1972)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255. IEEE (2009)
Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: AAAI, vol. 33, pp. 3558–3565 (2019)
Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)
Goode, A., Gilbert, B., Harkes, J., Jukic, D., Satyanarayanan, M.: OpenSlide: a vendor-neutral software foundation for digital pathology. J. Pathol. Inform. 4 (2013)
Heagerty, P.J., Zheng, Y.: Survival model predictive accuracy and ROC curves. Biometrics 61(1), 92–105 (2005)
Jiang, J., Wei, Y., Feng, Y., Cao, J., Gao, Y.: Dynamic hypergraph neural networks. In: IJCAI, pp. 2635–2641. AAAI Press (2019)
Kandoth, C.: Mutational landscape and significance across 12 major cancer types. Nature 502(7471), 333 (2013)
Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457–481 (1958)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph CNN for survival analysis on whole slide pathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 174–182. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_20
Mayr, A., Schmid, M.: Boosting the concordance index for survival data-a unified framework to derive and evaluate biomarker combinations. PLoS One 9(1), e84483 (2014)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
Yang, Y., Zou, H.: A cocktail algorithm for solving the elastic net penalized Coxs regression in high dimensions. Stat. Interface 6(2), 167–173 (2013)
Yao, J., Zhu, X., Huang, J.: Deep multi-instance learning for survival prediction from whole slide images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 496–504. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_55
Yao, J., Zhu, X., Zhu, F., Huang, J.: Deep correlational learning for survival prediction from multi-modality data. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 406–414. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_46
Zhu, X., Yao, J., Huang, J.: Deep convolutional neural network for survival analysis with pathological images. In: BIBM, pp. 544–547. IEEE (2016)
Zhu, X., Yao, J., Zhu, F., Huang, J.: WSISA: making survival prediction from whole slide histopathological images. In: CVPR, pp. 7234–7242 (2017)
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
Di, D., Li, S., Zhang, J., Gao, Y. (2020). Ranking-Based Survival Prediction on Histopathological Whole-Slide Images. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-59722-1_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59721-4
Online ISBN: 978-3-030-59722-1
eBook Packages: Computer ScienceComputer Science (R0)
