Terasaki, Gbocho M

English

VCU Scholars Compass

Virginia Commonwealth University VCU Scholars Compass Biology and Medicine Through Mathematics Conference 2022 May 18th, 5:00 PM - 7:00 PM Mathematical Modeling of Brain Cancer Growth Using a Level-Set Method Gbocho M. Terasaki University of California, Merced, gterasaki@ucmerced.edu Follow this and additional works at: https://scholarscompass.vcu.edu/bamm  Part of the Life Sciences Commons, Medicine and Health Sciences Commons, Numerical Analysis and Computation Commons, and the Partial Differential Equations Commons https://scholarscompass.vcu.edu/bamm/2022/wed/54 This Event is brought to you for free and open access by the Dept. of Mathematics and Applied Mathematics at VCU Scholars Compass. It has been accepted for inclusion in Biology and Medicine Through Mathematics Conference by an authorized administrator of VCU Scholars Compass. For more information, please contact libcompass@vcu.edu. Title: Mathematical Modeling of Brain Cancer Growth Using a Level-Set Method  Glioblastoma multiforme (GBM) is one of the fastest-growing brain tumors and it has very low survival rates. Mathematical modeling can be used to predict the growth and treatment of brain cancer. However, one of the difficulties lies in the ability to estimate patient-specific parameters in the mathematical model from magnetic resonance imaging (MRI) data. In this poster, I will present a methodology that we developed based on a Level-Set method that solves the reaction-diffusion equation with randomly assigned parameters values on a brain geometry derived from MRI. Using information about the size of the different glioma sub-regions, we are developing a method that estimates the patient-specific model parameters so that we can predict the overall survival of a patient from a single pre-operative scan.   

Mathematical Modeling of Brain Cancer Growth Using a Level-Set Method

https://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1472&context=bamm

Mathematical Modeling of Brain Cancer Growth Using a Level-Set Method

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