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International Conference on Mathematical Biology and

Annual Meeting of The Society for Mathematical Biology,

July 27-30, 2009

University of British Columbia, Vancouver

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Program

Poster PS16A
Gargi Chakraborty
University of Washington
Title Predicting metabolic growth patterns from patient-specific anatomic imaging and mathematical modeling of glioblastomas
Abstract Glioblastomas (GBMs) are World Health Organization classified – grade IV brain tumors exemplified by their ability to rapidly proliferate and diffusely infiltrate surrounding tissue. Harsh microenvironments have been implicated in increasingly aggressive behavior of gliomas, which leads to lower survival across the brain tumor patient population (1). We present a metabolic mathematical model based on five partial differential equations to assess how angiogenesis or formation of nascent, irregular vasculature and hypoxia or phenomenon of oxygen depletion drive anatomical growth of GBMs. This expands upon the original model by Swanson that represented only the tumor cell population (2). The populations deterministically simulated include tumor cells, hypoxic cells, necrotic cells, vasculature, and tumor angiogenic factors. This model incorporates two patient-specific parameters, diffusion (D) and proliferation (ρ), derived from tumor volume measurement of two MRI scans conducted prior to treatment. We show that the resulting two-dimensional simulations parallel clinical imaging such as gadolinium-labeled T1-weighted MRI, T2-weighted MRI, and hypoxia-detecting [18F]-Fluoromisonidazole (FMISO) labeled PET imaging. We quantitatively compare the virtual and true MRI and FMISO-PET scans to assess how well the model predicts clinical imaging and whether it can be utilized as a tool to predict hypoxic growth patterns from MRI-based parameters.

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1. Szeto MD, Chakraborty G, Hadley J, Rockne R, Muzi M, Alvord EC Jr, Krohn KA, Spence AM, Swanson KR. Quantitative Metrics of Net Proliferation and Invasion Link Biological Aggressiveness Assessed by MRI with Hypoxia Assessed by FMISO-PET in Newly Diagnosed Glioblastomas. Cancer Research. 69: (10). 2009.

2. Swanson KR, Alvord EC Jr, Murray JD. Virtual brain tumours (gliomas) enhance the reality of medical imaging and highlight inadequacies of current therapy. British Journal of Cancer. 86,14-18. 2002.
CoauthorsStanley Gu, Russell Rockne, Kristin Swanson
LocationWoodward Lobby (Monday-Tuesday)