Abstract
Lung cancer has caused enormous harm to human life and traditional whole slide image (WSI) based lung cancer survival prediction methods suffer from information loss and can not maintain the spatial context of the images, which may play the important roles into survival analysis. Meanwhile, the impact of the heterogeneity between the medical images and the natural images has been noticed for some pre-trained models on medical image representation learning. In this paper, we proposed a Context Aware Lung Cancer Survival Prediction Network (CA-SurvNet) by using the whole slide images, in which the survival prediction is decided by every patch of a WSI and its associated spatial context as well. Specifically, the representation of every WSI patch is first learned via a self-supervised learning based feature extractor, and then are sequentially concatenated followed by a channel-wisely dimensional reduction to preserve the significant information and maintain the spatial structure of the WSI simultaneously. Extensive experiments on two large benchmark datasets validate the superiority of the proposed method to its state-of-the-art competitors, and also its effectiveness of the WSI context preserving into the lung cancer survival prediction.
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References
Sung, H., Ferlay, J., Siegel, R.L., et al.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 71(3), 209–249 (2021)
Pantanowitz, L., Valenstein, P.N., Evans, A.J., et al.: Clinical Statistics: Introducing Clinical Trials, Survival Analysis, and Longitudinal Data Analysis. CRC Press, 2nd ed. edn. (2018)
Pantanowitz, L., Valenstein, P.N., Evans, A.J., et al.: Review of the current state of whole slide imaging in pathology. J Pathol Inform 2, 36 (2011)
Zhu, X., Yao, J., Zhu, F., et al.: WSISA: making survival prediction from whole slide histopathological images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7234–7242 (2017)
Li, Y., Zhang, Y., Liang, X., et al.: Risk-aware survival time prediction from whole slide pathological images using deep learning. Sci. Rep. 12(1), 1–13 (2022)
Fan, L., Sowmya, A., Meijering, E., et al.: Cancer survival prediction from whole slide images with self-supervised learning and slide consistency. IEEE Trans. Med. Imaging 42(1), 1–14 (2023)
Li, Y., Yao, J., Xu, Z., et al.: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. In: Medical Image Computing and Computer Assisted Intervention, pp. 290–298 (2018)
Yao, J., Zhu, X., Huang, J.: Deep multi-instance learning for survival prediction from whole slide images. In: Medical Image Computing and Computer-Assisted Intervention, pp. 505–513 (2019)
Chuang, W.Y., Chang, S.H., Yu, W.H., et al.: Successful identification of nasopharyngeal carcinoma in nasopharyngeal biopsies using deep learning. Cancers 12(2), 507 (2020)
Coudray, N., Ocampo, P.S., Sakellaropoulos, T., et al.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559–1567 (2018)
Bejnordi, B.E., Veta, M., Van Diest, P.J., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 (2017)
Wang, S., Chen, A., Yang, L., et al.: Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci. Rep. 8(1), 10393 (2018)
Parker, S.C.J., Khan, A., Talhouk, A., et al.: Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35(14), i446–i454 (2019)
Chen, R.J., Lu, M.Y., Weng, W.H., et al.: Multimodal co-attention transformer for survival prediction in gigapixel whole slide images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4015–4025 (2021)
Chen, T., Kornblith, S., Norouzi, M., et al.: A simple framework for contrastive learning of visual representations, pp. 1597–1607 (2020)
Salton, G., Wong, A.: Similarity measures. The SMART Retrieval Syst.: Exper. Autom. Document Process. 1, 145–159 (1970)
Katzman, J.L., Shaham, U., Cloninger, A., et al.: Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 24 (2018)
Cox, D.: Regression models and life-tables. J. Roy. Stat. Soc.: Ser. B (Methodol.) 34(2), 187–220 (1972)
Network, T.: Comprehensive molecular characterization of human colon and rectal cancer. Nature 487(7407), 330–337 (2012)
Team, N.L.S.T.R.: The national lung screening trial: overview and study design. Radiology 258(1), 243–253 (2011)
Klambauer, G., Unterthiner, T., Mayr, A., et al.: Self-normalizing neural networks. Adv. Neural. Inf. Process. Syst. 30, 971–980 (2017)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. ArXiv Preprint ArXiv:1412.6980, pp. 1–15 (2014)
Harrell, F.E., Jr., Lee, K.L., Mark, D.B.: Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15(4), 361–387 (1996)
Chang, J.R., Lee, C.Y., Chen, C.C., Reischl, J., Qaiser, T., Yeh, C.Y.: Hybrid aggregation network for survival analysis from whole slide histopathological images. In: de Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part V, pp. 731–740. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_70
Tang, B., Li, A., Li, B., et al.: Capsurv: capsule network for survival analysis with whole slide pathological images. IEEE Access 7, 26022–26030 (2019)
Acknowledgements
This work was partially supported by the General Program of National Natural Science Foundation of China under Grant 62276189, and the Fundamental Research Funds for the Central Universities No. 22120220583.
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Liu, X., Wang, Y., Luo, Y. (2024). A Context Aware Lung Cancer Survival Prediction Network by Using Whole Slide Images. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1964. Springer, Singapore. https://doi.org/10.1007/978-981-99-8141-0_28
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DOI: https://doi.org/10.1007/978-981-99-8141-0_28
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