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The Washington Research Foundation Fellowship
Mindy Szeto, Biochemistry, Sociology - 2008-09 WRFF
I was, and still am, amazed by the countless opportunities available for UW undergraduates to conduct cutting-edge research. Shortly after coming to the university through the Early Entrance Program, I knew I wanted to be involved in a project that could complement my science classes and allow me to address and explore my varied interests.
For the past three years, I have worked closely with Dr. Kristin Swanson in the Department of Pathology on the mathematical modeling of glioma (a type of brain tumor) in vivo growth and response to therapy. The lab's research is uniquely interdisciplinary; my current project combines aspects of neuropathology and biomedical technology to analyze imaging data obtained from patients receiving treatment at the UW Medical Center. The results could directly impact the development of clinical applications in the near future, which is very exciting!
Through Dr. Swanson's guidance and mentorship, I have gained a personal introduction to the world of academic research. My experience in the lab has been the most substantial contributor to my education thus far – even more than the sum total of my coursework. I hope to apply the skills I have cultivated towards completing an MD/PhD degree and pursuing a career in oncology research.
Mentor: Kristin Swanson, Pathology, Applied Mathematics
Project Title: Mathematical Modeling: A Novel Tool for Analysis of Glioma Growth Kinetics
Abstract: Gliomas, the most common primary brain tumors, are extremely aggressive and uniformly fatal, recurring inevitably despite treatment by surgical resection, radiation therapy, and chemotherapy. This is especially true of high-grade, rapidly growing glioblastoma multiforme (GBM) which account for nearly half of all gliomas. Current imaging modalities are unable to assess the full extent of diffuse glioma cell invasion. To more accurately understand the dynamics of GBM, Dr. Kristin Swanson has developed a mathematical model incorporating the diffuse invasion (migration rate D) and proliferation (ρ) of glioma cells. We have collected a database of patients with two serial magnetic resonance imaging (MRI) prior to any treatment or surgery. By measuring tumor volumes on these images and assuming a spherical volume, a mean radius and volume for each imaging date can be found. The radial rate of growth, which is linear according to the model, can then be calculated for the determination of the patient-specific model parameters D and ρ. My research aims to evaluate the influence of anatomical location on the model parameters, D and ρ. The variability of D, ρ, and tumor growth velocities are functions of the glioma’s spatial location in the brain, with respect to distributions of white and grey matter. 3D spatial computer simulations will be utilized to illustrate growth kinetics and observed spatial variability. Based on sets of model parameter values, a simulation algorithm can create visualizations of model-predicted untreated tumor growth. Comparisons of the simulation results to observed patient progress will serve to further validate the model technique, with the model prediction as a virtual control. This project is a direct response to published reports citing tumor location relating to patient survival. Model simulations have the unique ability to test this hypothesis, and to more clearly refine and investigate the role of anatomical influence on glioma growth kinetics.