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Ann Biomed Eng. 2016 Sep;44(9):2626-41. doi: 10.1007/s10439-016-1691-6. Epub 2016 Jul 6.

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

Author information

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.yankeelov@utexas.edu.
2
Department of Surgery and Computation Institute, The University of Chicago, Chicago, IL, USA.
3
Institut de Mathématiques de Bordeaux, Université de Bordeaux and INRIA, Bordeaux, France.
4
Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
5
Departments of Biomedical Engineering and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
6
Pharma Research and Early Development, Clinical Pharmacology, F. Hoffmann-La Roche Ltd, Basel, Switzerland.
7
Clinical Pharmacology and DMPK, MedImmune, Gaithersburg, MD, USA.
8
Center for Bioinformatics and Systems Biology, Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
9
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
10
Department of Biomedical Engineering, Watson School of Engineering and Applied Science, Binghamton University, State University of New York, Binghamton, NY, USA.
11
Departments of Mechanical Engineering and Materials Science, and Neurological Surgery, Washington University in St. Louis, St. Louis, MO, USA.

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.

KEYWORDS:

Agent-based modeling; Cancer; Cancer screening; Computational modeling; Epidemiology; Mathematical modeling; Numerical modeling; Predictive oncology

PMID:
27384942
PMCID:
PMC4983505
DOI:
10.1007/s10439-016-1691-6
[Indexed for MEDLINE]
Free PMC Article

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