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The Levinson Emerging Scholars Program
Rita Sodt - Computer Science
Rita Sodt began her research during her first year at the University of Washington. She thinks getting involved with research early on in her undergraduate studies was one of the best decisions she made because it has really enriched her undergraduate experience and helped her discover where her interests lie. Rita came to work in the Swanson lab because she was excited by its unique approach to cancer research. Using a mathematical model to simulate brain tumor growth it is possible to make predictions about how a tumor would spread; predictions that can lead to improved tumor treatments. Early on in her research, Rita gravitated towards the programming problems in the lab, including image analysis and simulations of tumor growth, and decided to major in computer science. She loves working in an interdisciplinary research setting, especially because as she is working on a computer science related project she gets to learn new things all the time about applied math and biology, and understand the big picture behind the research. She plans to pursue graduate studies in computer science and continue research related to computer science and health care.
Mentor: Dr. Kristin Swanson, Pathology
Project Title: Simulation of Anisotropic Growth of Gliomas Using Diffusion Tensor Imaging
Abstract: Gliomas are highly invasive brain tumors that account for nearly half of all primary brain tumors. Since current medical imaging techniques only detect a portion of these cancerous cells, a computational model was developed by Dr. Kristin Swanson to give more information about the extent of the tumor invasion below the threshold of imaging and to give a prediction of glioma growth that can be tailored to individual patient's tumor. This computational model is currently based on two elements: cell proliferation and isotropic cell diffusion. Isotropic diffusion assumes that cell migration is random, however it is commonly accepted that glioma cells migrate preferentially along the direction of white matter tracts. In order to account for this observed diffusion, I will write a program that calculates the growth of gliomas that includes anisotropic diffusion. To do this I will use a published technique utilizing diffusion tensor imaging (DTI) to show the directional orientation of brain matter throughout the brain, which indicates the direction that glioma cells tend to migrate. My project will result in an improved mathematical model that can be used to simulate 3D virtual tumors. Hopefully after modifying the model to include anisotropic cell diffusion, the simulated tumors will more closely predict the growth of tumors that we observe in vivo. I will compare the results of our simulations to observed tumor growth to determine how well the model predicts the growth of gliomas.