Abstract
With more than 1,800,000 cases and over 862,000 deaths per year, metastatic colorectal cancer is the second leading cause of cancer related deaths in modern societies. The estimated patient survival is one of the main factors for therapy adjustment. While proportional hazard models are a key instrument for survival analysis within the last centuries, the assumption of hazard proportionality might be overly restrictive and their applicability to complex data remains difficult. Especially the integration of image data comes at the cost of a careful pre-selection of hand-crafted features only. With the rise of deep learning, directly differentiable models for survival analysis have been developed. While some inherit the difficulties of the proportionality assumption, others are restricted to scalar data input. Computed-tomography-based survival analysis remains a hardly researched topic at all. We propose a deep model for computed-tomography-based survival analysis providing a hazard probability output representation comparable to Cox regression without relying on the hazard proportionality assumption. The model is evaluated on multiple datasets, including metastatic colorectal cancer computed tomography imaging data, and significantly reduces the average prediction error compared to the Cox proportional hazards model.
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References
Surveillance, Epidemiology, and End Results (SEER) program (www.seer.cancer.gov) research data (1973–2015), National Cancer Institute, DCCPS, Surveillance Research Program, released April 2018, based on the November 2017 submission (2017)
American Cancer Society: Cancer Facts and Figures. American Cancer Society, Atlanta (2019)
National Cancer Institute overall survival (2019). www.cancer.gov/publications/dictionaries/cancer-terms/def/os. Accessed 01 Apr 2019
Aerts, H.J., et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014)
Brierley, J.D., Gospodarowicz, M.K., Wittekind, C.: TNM Classification of Malignant Tumours. Wiley, Hoboken (2016)
Cohen, S.J., et al.: Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. Clin. Oncol. 26, 3213–3221 (2008)
Efron, B.: Bootstrap methods: another look at the Jackknife. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. SSS, pp. 569–593. Springer, New York (1992). https://doi.org/10.1007/978-1-4612-4380-9_41
Ferlay, J., et al.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–E386 (2015)
Greenspan, H., Van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)
Haarburger, C., Weitz, P., Rippel, O., Merhof, D.: Image-based survival analysis for lung cancer patients using CNNs. arXiv preprint arXiv:1808.09679 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ishwaran, H., Kogalur, U.B., Blackstone, E.H., Lauer, M.S., et al.: Random survival forests. Ann. Appl. Stat. 2(3), 841–860 (2008)
Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: Deep survival: a deep cox proportional hazards network. Stat 1050, 2 (2016)
Katzmann, A., et al.: Predicting lesion growth and patient survival in colorectal cancer patients using deep neural networks (2018)
Lao, J., et al.: A deep learning-based radiomics model for prediction of survival in Glioblastoma Multiforme. Sci. Rep. 7(1), 10353 (2017)
Lee, C., Zame, W.R., Yoon, J., van der Schaar, M.: DeepHit: a deep learning approach to survival analysis with competing risks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Miller Jr., R.G.: Survival Analysis, vol. 66. Wiley, Hoboken (2011)
Ries, L.A.G., et al.: Cancer incidence and survival among children and adolescents: United States SEER Program 1975–1995. Cancer incidence and survival among children and adolescents: United States SEER Program 1975–1995 (1999)
Rossi, P.H., Berk, R.A., Lenihan, K.J.: Money, work and crime: some experimental results (1980)
Schemper, M.: Cox analysis of survival data with non-proportional hazard functions. J. R. Stat. Soc.: Ser. D (Stat.) 41(4), 455–465 (1992)
Zauber, A.G., et al.: Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. New Engl. J. Med. 366(8), 687–696 (2012)
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Katzmann, A. et al. (2019). Computed Tomography Image-Based Deep Survival Regression for Metastatic Colorectal Cancer Using a Non-proportional Hazards Model. In: Rekik, I., Adeli, E., Park, S. (eds) Predictive Intelligence in Medicine. PRIME 2019. Lecture Notes in Computer Science(), vol 11843. Springer, Cham. https://doi.org/10.1007/978-3-030-32281-6_8
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DOI: https://doi.org/10.1007/978-3-030-32281-6_8
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