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

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

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...

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Bibliographic Details
Published in:Annals of biomedical engineering Vol. 44; no. 9; pp. 2626 - 2641
Main Authors: Yankeelov, Thomas E., An, Gary, Saut, Oliver, Luebeck, E. Georg, Popel, Aleksander S., Ribba, Benjamin, Vicini, Paolo, Zhou, Xiaobo, Weis, Jared A., Ye, Kaiming, Genin, Guy M.
Format: Journal Article
Language:English
Published: New York Springer US 01-09-2016
Springer Nature B.V
Springer Verlag
Subjects:
Analysis of PDEs
Barriers
Biochemistry
Biological and Medical Physics
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Biophysics
Cancer
Classical Mechanics
Computation
Computer Science
Hierarchies
Humans
Life Sciences
Mathematical models
Mathematics
Medical Oncology
Modeling and Simulation
Models, Biological
Multi-Scale Modeling in the Clinic
Neoplasms
State of the art
Strategy
Synthesis
Numerical modeling
Computational modeling
Predictive oncology
Agent-based modeling
Mathematical modeling
Epidemiology
Cancer
Cancer screening
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Description
Summary: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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ISSN:0090-6964
1573-9686
DOI:10.1007/s10439-016-1691-6