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Title page for ETD etd-03182016-140507

Type of Document Dissertation
Author Hormuth, David Andrew II
Author's Email Address davidhormuth@gmail.com
URN etd-03182016-140507
Title Predicting the Spatio-Temporal Evolution of Tumor Growth and Treatment Response in a Murine Model of Glioma
Degree PhD
Department Biomedical Engineering
Advisory Committee
Advisor Name Title
Michael I. Miga Committee Co-Chair
Thomas E. Yankeelov Committee Co-Chair
C. Chad Quarles Committee Member
John C. Gore Committee Member
Vito Quaranta Committee Member
  • radiotherapy
  • cancer
  • mathematical modeling
  • reaction-diffusion
  • magnetic resonance imaging
Date of Defense 2016-03-16
Availability unrestricted
Glioblastoma is a highly invasive and aggressive brain tumor which accounts for nearly 82% of all gliomas. Patients with glioblastoma typically have a poor prognosis, suffering recurrence 7 to 10 months from the conclusion of adjuvant therapy. One promising direction for improving the clinical care of cancer, in general, and glioblastomas, in particular, is the development of accurate and precise predictive mathematical models of tumor growth. Through the use of non-invasive imaging data, mathematical models can be parameterized by the unique characteristics of an individual’s tumor to provide a “forecast” of future tumor growth and treatment response. However, there is currently a paucity of mathematical models that have been evaluated in a controlled setting where model predictions can be validated directly to experimental results. In this work, an experimental and modeling framework is developed in which imaging measurements acquired before and after treatment are used to inform individualized biophysical models of glioma growth and response to radiotherapy. For untreated tumor growth, this work demonstrated that the commonly used reaction-diffusion model poorly predicts in vivo glioma growth. Secondly, this work demonstrated that mechanobiological effects are a necessary component to brain tumor modeling. Thirdly, this work showed that a variable carrying capacity is needed to capture the intra-tumoral spatial heterogeneity. Finally, a model was developed incorporating rapid cell death and reduced cellular proliferation and was shown to accurately predict future tumor growth following whole brain radiotherapy. Together, these studies show the potential power that image driven individualized tumor “forecasts” could have on improving the clinical care of cancer.
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