Abstract
Glioblastoma is the most aggressive tumor originated in the central nervous system. Modeling its evolution is of great interest for therapy planning and early response to treatment assessment. Using a continuous multi-scale growth model, which considers the angiogenic process, oxygen supply and different phenotype expressions, a new method is proposed for setting the initial values of the celular variables, based on a spatiotemporal characterization of their distribution in controlled synthetic simulations. The method is applied to a real case showing an improvement on the dynamic stability, compared to the usual method.
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References
Harter, D.H., et al.: Glioblastoma multiforme: state of the art and future therapeutics. Surg. Neurol. Int. 5(1), 64 (2014)
Kumar, V., et al.: Robbins and Cotran Pathologic Basis of Disease. Elsevier Saunders, Philadelphia (2005)
Tang, L., et al.: Computational modeling of 3D tumor growth and angiogenesis for chemotherapy evaluation. PLoS ONE 9(1), e83962 (2014)
Swanson, K.R., et al.: Quantifying the role of angiogenesis in malignant progression of gliomas: in silico modeling integrates imaging and histology. Cancer Res. 71(24), 7366–7375 (2011)
Cai, Y., et al.: Coupled modelling of tumour angiogenesis, tumour growth and blood perfusion. J. Theor. Biol. 279(1), 90–101 (2011)
Martnez-Gonzlez, A., et al.: Hypoxic cell waves around necrotic cores in glioblastoma: a biomathematical model and its therapeutic implications. Bull. Math. Biol. 74(12), 2875–2896 (2012)
Saut, O., et al.: A multilayer grow-or-go model for gbm: effects of invasive cells and anti-angiogenesis on growth. Bull. Math. Biol. 76(9), 2306–2333 (2014)
Gerlee, P., et al.: The impact of phenotypic switching on glioblastoma growth and invasion. PLoS Comput. Biol. 8(6), e1002556 (2012)
Manoussaki, D.: A mechanochemical model of angiogenesis and vasculogenesis. ESAIM. Math. Model. Numer. Anal. 37(04), 581–599 (2003)
Marias, K., et al.: A proposed paradigm shift in initializing cancer predictive models with dce-mri based pk parameters: a feasibility study. Cancer Inf. 14(Suppl. 4), 7 (2015)
Eikenberry, S.E., et al.: Virtual glioblastoma: growth, migration and treatment in a three-dimensional mathematical model. Cell Prolif. 42(4), 511–528 (2009)
Jewell, U.R.: Induction of HIF-1a in response to hypoxia is instantaneous. FASEB J. 15(7), 1312–1314 (2001)
Vaupel, P.: The role of hypoxia-induced factors in tumor progression. Oncologist 9(Suppl. 5), 10–17 (2004)
Martin Brown, J., et al.: Exploiting tumour hypoxia in cancer treatment. Nat. Rev. Cancer 4(6), 437–447 (2004)
McLellan, S.A., et al.: Oxygen delivery and haemoglobin. Continuing Educ. Anaesth. Crit. Care Pain 4(4), 123–126 (2004)
Grimes, D.R., et al.: A method for estimating the oxygen consumption rate in multicellular tumour spheroids. J. Roy. Soc. Interface 11(92), 20131124 (2014)
Rockne, R., et al.: Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach. Phys. Med. Biol. 55(12), 3271–3285 (2010)
Harpold, H.L.P., et al.: The evolution of mathematical modeling of glioma proliferation and invasion. J. Neuropathol. Exp. Neurol. 66(1), 1–9 (2007)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Acknowledgments
This work was partially supported by Project TIN2013- 43457-R funded by the Ministerio de Economia y Competitividad of Spain, the GLIOMARKERS project funded by the INBIO joint action between UPV and IIS HUPLF, and the CURIAM-FDFT project funded by ITACA-UPV EMBLEMA action. E. Fuster-Garcia acknowledges the financial support from the UPV PAID-10–14 grant.
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Ortiz-Pla, J., Fuster-Garcia, E., Juan-Albarracin, J., Garcia-Gomez, J.M. (2016). GBM Modeling with Proliferation and Migration Phenotypes: A Proposal of Initialization for Real Cases. In: Tsaftaris, S., Gooya, A., Frangi, A., Prince, J. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2016. Lecture Notes in Computer Science(), vol 9968. Springer, Cham. https://doi.org/10.1007/978-3-319-46630-9_7
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DOI: https://doi.org/10.1007/978-3-319-46630-9_7
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