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Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

  • Multi-Scale Modeling in the Clinic
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Abstract

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.

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Image credits: Top center and top right: Ref.100; bottom left and bottom center: Invivosciences, LLC. All images used with permission.

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Notes

  1. http://bidd.nus.edu.sg/group/cjttd/.

  2. https://www.pharmgkb.org/.

  3. http://www.imagwiki.nibib.nih.gov/content/committee-credible-practice-modeling-simulation-healthcare-description.

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Acknowledgments

The authors thank the NIH for funding through U01CA174706 (TEY), 1R01-GM-115839 and P30-DK-42086 (GA), 1U01CA166886 (XZ), French National Research Agency ANR-10-IDEX-03-02 (OS), R01CA138264 (ASP), U01CA152926 (EGL), U01EB016422 (GMG), and CPRIT RR160005 (T.E.Y.). T.E.Y. is a CPRIT Scholar in Cancer Research.

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Authors and Affiliations

  1. Departments of Biomedical Engineering and Internal Medicine, Institute for Computational and Engineering Sciences, Cockrell School of Engineering, The University of Texas at Austin, 107 W. Dean Keeton, BME Building, 1 University Station, C0800, Austin, TX, 78712, USA

    Thomas E. Yankeelov

  2. Department of Surgery and Computation Institute, The University of Chicago, Chicago, IL, USA

    Gary An

  3. Institut de Mathématiques de Bordeaux, Université de Bordeaux and INRIA, Bordeaux, France

    Oliver Saut

  4. Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    E. Georg Luebeck

  5. Departments of Biomedical Engineering and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Aleksander S. Popel

  6. Pharma Research and Early Development, Clinical Pharmacology, F. Hoffmann-La Roche Ltd, Basel, Switzerland

    Benjamin Ribba

  7. Clinical Pharmacology and DMPK, MedImmune, Gaithersburg, MD, USA

    Paolo Vicini

  8. Center for Bioinformatics and Systems Biology, Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA

    Xiaobo Zhou

  9. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA

    Jared A. Weis

  10. Department of Biomedical Engineering, Watson School of Engineering and Applied Science, Binghamton University, State University of New York, Binghamton, NY, USA

    Kaiming Ye

  11. Departments of Mechanical Engineering and Materials Science, and Neurological Surgery, Washington University in St. Louis, St. Louis, MO, USA

    Guy M. Genin

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  1. Thomas E. Yankeelov
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  2. Gary An
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Correspondence to Thomas E. Yankeelov.

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Associate Editor Joel D. Stitzel oversaw the review of this article.

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Yankeelov, T.E., An, G., Saut, O. et al. Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success. Ann Biomed Eng 44, 2626–2641 (2016). https://doi.org/10.1007/s10439-016-1691-6

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  • DOI: https://doi.org/10.1007/s10439-016-1691-6

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