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Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details

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Cancer Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1711))

Abstract

Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth.

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Acknowledgments

This work was supported through funding from CPRIT RR160005 and the National Cancer Institute U01CA174706, K25CA204599, and R01CA186193, from the National Institute of Neurological Disorders and Stroke R01NS049251 and the Vanderbilt-Ingram Cancer Center Support Grant (NIH P30CA68485).

Author information

Authors and Affiliations

  1. Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA

    David A. Hormuth II, Stephanie L. Eldridge & Thomas E. Yankeelov

  2. Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA

    Stephanie L. Eldridge & Thomas E. Yankeelov

  3. Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Jared A. Weis

  4. Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC, USA

    Jared A. Weis

  5. Biomedical Engineering, Vanderbilt University, Nashville, TN, USA

    Michael I. Miga

  6. Department of Radiology, Vanderbilt University, Nashville, TN, USA

    Michael I. Miga

  7. Department of Radiological Sciences, Vanderbilt University, Nashville, TN, USA

    Michael I. Miga

  8. Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA

    Michael I. Miga & Thomas E. Yankeelov

  9. Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA

    Thomas E. Yankeelov

Authors
  1. David A. Hormuth II
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  2. Stephanie L. Eldridge
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  3. Jared A. Weis
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  4. Michael I. Miga
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  5. Thomas E. Yankeelov
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Corresponding authors

Correspondence to David A. Hormuth II or Thomas E. Yankeelov .

Editor information

Editors and Affiliations

  1. NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Louise von Stechow

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Hormuth, D.A., Eldridge, S.L., Weis, J.A., Miga, M.I., Yankeelov, T.E. (2018). Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details. In: von Stechow, L. (eds) Cancer Systems Biology. Methods in Molecular Biology, vol 1711. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7493-1_11

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  • DOI: https://doi.org/10.1007/978-1-4939-7493-1_11

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7492-4

  • Online ISBN: 978-1-4939-7493-1

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