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Predicting Glioblastoma Growth Using a Poisson Process


Abstract In this research we consider stochastic models of Glioblastoma Multiforme brain tumors. We first look at a model by K. Swanson et al., which describes the dynamics as random diffusion plus deterministic logistic growth. We introduce a stochastic component in the logistic growth in the form of a random growth rate defined by a Poisson process. We show that this stochastic logistic growth model leads to a more accurate evaluation of the tumor growth compared its deterministic counterpart. We also discuss future plans to incorporate individual patient geometry, extend the model to three dimensions and to incorporate effects of different treatments into our model, in collaboration with a local hospital.
Created Date 2013-12
Contributor Manning, Michael Clare (Author) / Kostelich, Eric (Thesis Director) / Kuang, Yang (Committee Member) / Gardner, Carl (Committee Member) / Barrett, The Honors College / School of Mathematical and Statistical Sciences / School of Letters and Sciences / School of Human Evolution and Social Change
Subject Brain Tumors / Glioblastoma Multiforme / Poisson Process
Series Academic Year 2013-2014
Type Text
Extent 26 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS


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