GBM Modeling with Proliferation and Migration Phenotypes: A Proposal of Initialization for Real Cases

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.

[1]  Xiaobo Zhou,et al.  Computational Modeling of 3D Tumor Growth and Angiogenesis for Chemotherapy Evaluation , 2014, PloS one.

[2]  Víctor M. Pérez-García,et al.  Hypoxic Cell Waves Around Necrotic Cores in Glioblastoma: A Biomathematical Model and Its Therapeutic Implications , 2012, Bulletin of Mathematical Biology.

[3]  Peter Vaupel,et al.  The role of hypoxia-induced factors in tumor progression. , 2004, The oncologist.

[4]  T. Walsh,et al.  Oxygen delivery and haemoglobin , 2004 .

[5]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[6]  Alexander R A Anderson,et al.  Quantifying the Role of Angiogenesis in Malignant Progression of Gliomas: in Silico Modeling Integrates Imaging and Histology Nih Public Access Author Manuscript Introduction , 2011 .

[7]  Thierry Colin,et al.  A Multilayer Grow-or-Go Model for GBM: Effects of Invasive Cells and Anti-Angiogenesis on Growth , 2014, Bulletin of mathematical biology.

[8]  J. Brown,et al.  Exploiting tumour hypoxia in cancer treatment , 2004, Nature Reviews Cancer.

[9]  Matthias A. Karajannis,et al.  Glioblastoma multiforme: State of the art and future therapeutics , 2014, Surgical neurology international.

[10]  E. Kostelich,et al.  Virtual glioblastoma: growth, migration and treatment in a three‐dimensional mathematical model , 2009, Cell proliferation.

[11]  David Robert Grimes,et al.  A method for estimating the oxygen consumption rate in multicellular tumour spheroids , 2014, Journal of The Royal Society Interface.

[12]  Kristin R. Swanson,et al.  The Evolution of Mathematical Modeling of Glioma Proliferation and Invasion , 2007, Journal of neuropathology and experimental neurology.

[13]  D. Manoussaki A mechanochemical model of angiogenesis and vasculogenesis , 2003 .

[14]  Kostas Marias,et al.  A Proposed Paradigm Shift in Initializing Cancer Predictive Models with DCE-MRI Based PK Parameters: A Feasibility Study , 2015, Cancer informatics.

[15]  K Hendrickson,et al.  Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach , 2010, Physics in medicine and biology.

[16]  Philip Gerlee,et al.  The Impact of Phenotypic Switching on Glioblastoma Growth and Invasion , 2012, PLoS Comput. Biol..

[17]  Quan Long,et al.  Coupled modelling of tumour angiogenesis, tumour growth and blood perfusion. , 2011, Journal of theoretical biology.

[18]  M. Gassmann,et al.  Induction of HIF–1α in response to hypoxia is instantaneous , 2001, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.