Abstract
Radiomics transform medical images into a rich source of information and a main tool for the tumor growth survey, which is the result of multiple processes at different scales composing a complex system. To model the tumor evolution in both time and space we propose to exploit radiomic features within a multi-scale architecture that models the biological events at different levels. The proposed framework is based on the HMM architecture that encodes the relation between radiomic features as observed phenomena and the mechanical interactions within the tumor as a hidden process. On the other hand, it models the Tumor evolution through time thanks to its dynamic aspect. While, to represent the biological interactions, we use a Hierarchical Bayesian Network where we associate a level for each scale (Tissue, cell-cluster, cell scale). Thus, the HMM induces a Dynamic Hierarchical Bayesian Network that encodes the tumor growth aspects and factors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jemal, A., et al.: Annual report to the nation on the status of cancer, 1975–2014, featuring survival. JNCI: J. Natl. Cancer Inst. 109(9), djx030 (2017). Clerk Maxwell, J.: A Treatise on Electricity and Magnetism, 3rd edn, vol. 2, pp. 68–73. Clarendon, Oxford (1892)
Malvezzi, M., et al.: European cancer mortality predictions for the year 2018 with focus on colorectal cancer. Ann. Oncol. 29(4), 1016–1022 (2018)
Hornberg, J.J., Bruggeman, F.J., Westerhoff, H.V., Lankelma, J.: Cancer: a systems biology disease. Biosystems 83(2–3), 81–90 (2006). https://doi.org/10.1016/j.biosystems.2005.05.014
Masoudi-Nejad, A., Wang, E.: Cancer modeling and network biology: accelerating toward personalized medicine. In: Seminars in Cancer Biology, vol. 30, pp. 1–3. Academic Press, February 2015
Feng, Y., Boukhris, S.J., Ranjan, R., Valencia, R.A.: Biological systems: multiscale modeling based on mixture theory. In: De, S., Hwang, W., Kuhl, E. (eds.) Multiscale Modeling in Biomechanics and Mechanobiology, pp. 257–286. Springer, London (2015). https://doi.org/10.1007/978-1-4471-6599-6_11
Masoudi-Nejad, A., Bidkhori, G., Ashtiani, S.H., Najafi, A., Bozorgmehr, J.H., Wang, E.: Cancer systems biology and modeling: microscopic scale and multiscale approaches. In: Seminars in Cancer Biology, vol. 30, pp. 60–69. Academic Press, February 2015
Ghadiri, M., Heidari, M., Marashi, S.A., Mousavi, S.H.: A multiscale agent-based framework integrated with a constraint-based metabolic network model of cancer for simulating avascular tumor growth. Mol. BioSyst. 13(9), 1888–1897 (2017)
Zhang, L., Lu, L., Summers, R.M., Kebebew, E., Yao, J.: Personalized pancreatic tumor growth prediction via group learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 424–432. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_48
Zhang, L., Lu, L., Summers, R.M., Kebebew, E., Yao, J.: Convolutional invasion and expansion networks for tumor growth prediction. IEEE Trans. Med. Imaging 37(2), 638–648 (2018)
Weizman, L., et al.: Prediction of brain MR scans in longitudinal tumor follow-up studies. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 179–187. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33418-4_23
Ibargüengoytia, P.H., Reyes, A., García, U.A., Romero, I., Pech, D.: Evaluating probabilistic graphical models for forecasting. In: 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP), pp. 1–6. IEEE, September 2015
Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521(7553), 452 (2015)
Lucas, P.: Bayesian networks in medicine: a model-based approach to medical decision making (2001)
Sucar, L.E., Bielza, C., Morales, E.F., Hernandez-Leal, P., Zaragoza, J.H., Larrañaga, P.: Multi-label classification with Bayesian network-based chain classifiers. Pattern Recogn. Lett. 41, 14–22 (2014)
Tauber, S., Navarro, D.J., Perfors, A., Steyvers, M.: Bayesian models of cognition revisited: setting optimality aside and letting data drive psychological theory. Psychol. Rev. 124(4), 410 (2017)
Pearl, J.: Bayesian networks (2011)
Forney, G.D.: The viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)
Wu, M., Yang, X., Chan, C.: A dynamic analysis of IRS-PKR signaling in liver cells: a discrete modeling approach. PLoS One 4(12), e8040 (2009)
Amiri, S., Rekik, I., Mahjoub, M.A.: Deep random forest-based learning transfer to SVM for brain tumor segmentation. In: 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 297–302. IEEE. March 2016
Amiri, S., Rekik, I., Mahjoub, M.A.: Bayesian network and structured random forest cooperative deep learning for automatic multi-label brain tumor segmentation. In: 2018 10th International Conference The International Conference on Agents and Artificial Intelligence (ICAART) (2018)
Amiri, S., Rekik, I., Mahjoub, M.A.: Dynamic multiscale tree learning using ensemble strong classifiers for multi-label segmentation of medical images with lesions. In: 2018 13th International Conference on Computer Vision Theory and Applications (VISAAP) (2018)
Amiri, S., Mahjoub, M.A., Rekik, I.: Dynamic multiscale tree learning using ensemble strong classifiers for multi-label segmentation of medical images with lesions. Neurocomputing (2018)
Gyftodimos, E., Flach, P.A.: Hierarchical bayesian networks: an approach to classification and learning for structured data. In: Vouros, G.A., Panayiotopoulos, T. (eds.) SETN 2004. LNCS (LNAI), vol. 3025, pp. 291–300. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24674-9_31
Rahman, M.M., Feng, Y., Yankeelov, T.E., Oden, J.T.: A fully coupled space-time multiscale modeling framework for predicting tumor growth. Comput. Methods Appl. Mech. Eng. 320, 261–286 (2017)
Zhu, S., Wang, Y.: Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks. Sci. Rep. 5, 17841 (2015)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Elsevier (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Amiri, S., Mahjoub, M.A. (2019). HMDHBN: Hidden Markov Inducing a Dynamic Hierarchical Bayesian Network for Tumor Growth Prediction. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-29888-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29887-6
Online ISBN: 978-3-030-29888-3
eBook Packages: Computer ScienceComputer Science (R0)