Bridging the Gap Between Modeling of Tumor Growth and Clinical Imaging
This chapter gives a brief overview of the biological processes involved in vascularized tumor growth, followed by a summary of recent mathematical modeling to simulate the biology of tumor growth and angiogenesis. It provides an overview of medical image
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Abstract This chapter gives a brief overview of the biological processes involved in vascularized tumor growth, followed by a summary of recent mathematical modeling to simulate the biology of tumor growth and angiogenesis. It provides an overview of medical image analysis and describes recent efforts in the area of coupling such tumor models with imaging data. We do not discuss the research of obtaining tumor-specific information from medical imaging data, for which extensive work has been done in image processing and signal analysis. The chapter concludes with a sample simulation of vascularized tumor growth showing the critical role of vascularization in tumor invasiveness and highlighting the potential of gaining further insight into tumor behavior from a more expansive future integration of 3D tumor models with clinical imaging data.
Introduction Modeling of tumor growth at the centimeter tissue-scale is typically represented using diffusion reaction equations describing the space and time dynamics of mass and diffusible substances (see recent reviews [1–8]). Parameters in these models can be coupled with biological and clinical data in order to more faithfully represent tumor growth and treatment response, including measurements from in vitro cell culture, intravital microscopy, and histopathology [9–16]. Data from medical
B. Abdollahi, Ph.D. Department of Electrical Engineering, University of Louisville, Louisville, KY 40208, USA N. Dunlap, M.D., Ph.D. Department of Radiation Oncology, University of Louisville, Louisville, KY 40202, USA H.B. Frieboes, Ph.D. (*) Department of Bioengineering, Lutz Hall 419, University of Louisville, Louisville, KY 40208, USA e-mail: [email protected] A.S. El-Baz et al. (eds.), Abdomen and Thoracic Imaging: An Engineering & Clinical Perspective, DOI 10.1007/978-1-4614-8498-1_18, © Springer Science+Business Media New York 2014
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images has also been employed to define values for tumor model parameter values [17–21]. Image series taken from patients contain visual information about anatomical and diffusion properties of tumor tissue. It is challenging, however, to incorporate biological and imaging information into mathematical models of tumor growth. A first step in this process is to synthesize the biological information so that it can be properly represented by mathematical models.
Onset of Cancer Healthy cells exist with an established pattern of cellular behavior and life expectancy. Each cell in an organism carries a full copy of the organism’s DNA, containing the instructions that determine the cell’s behavior. Numerous endogenous as well as exogenous mechanisms exist to ensure that cells maintain homeostasis and preserve the integrity of their nuclear DNA in order to maximize the success of an organism’s life. For cancer to occur, several of these mechanisms have to jointly fail in order for a cell to start rapid and sustained proliferation with a degenerate DNA or avoid senescence [22]. These mechanisms include cellinduced apoptosi
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