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Abstract #2200

Parameter Estimation in a Mathematical Model of Murine Glioma from MR Imaging

Eric J Kostelich1, Erica M Rutter2, Tracy L Stepien3, Barrett J Anderies1, Jonathan D Plasencia4, Eric C Woolf5, Adrienne C Scheck5, Gregory H Turner6, Qingwei Liu6, David Frakes4, Vikram Kodibagkar4, Yang Kuang1, and Mark C Preul7

1Mathematical & Statistical Sciences, Arizona State University, Tempe, AZ, United States, 2Mathematics, North Carolina State University, Raleigh, NC, United States, 3Mathematics, University of Arizona, Tucson, AZ, United States, 4Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 5Neuro-Oncology Research, Barrow Neurological Institute, Phoenix, AZ, United States, 6BNI-ASU Center for Preclinical Imaging, Barrow Neurological Institute, Phoenix, AZ, United States, 7Neurosurgery Research, Barrow Neurological Institute, Phoenix, AZ, United States

This study assesses the feasibility of estimating and quantifying the uncertainty in growth parameters for a mathematical model of glioma growth from MR imaging. Five immunocompetent albino mice were inoculated intracranially with syngeneic GL261 tumor cells and followed by serial imaging for 25 days. We simulated the growth of the tumor from the known initial conditions using a popular two-parameter reaction-diffusion model and compared the results with the imaging. Our simulations show that the growth and diffusion rates in the model cannot be identified from imaging data alone. Uncertainty quantification in model predictions of the tumor is problematic.

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