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Stochastic parameterization of the proliferation-diffusion model of brain cancer in a Murine model

Abstract Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far... (more)
Created Date 2016-05
Contributor Anderies, Barrett James (Author) / Kostelich, Eric (Thesis Director) / Kuang, Yang (Committee Member) / Stepien, Tracy (Committee Member) / Harrington Bioengineering Program / School of Mathematical and Statistical Sciences / Barrett, The Honors College
Subject Brain Tumors / MRI / Stochastic / Ensemble Simulation / Gl261 / Mathematical Modeling / Murine Glioblastoma Multiforme / Proliferation-diffusion Model / Anisotropy / Central Finite Differences / Jaccard Distance / ODE45 / Cancer
Series Academic Year 2015-2016
Type Text
Extent 38 pages
Language English
Reuse Permissions All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS

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