YANKEELOV, Thomas E.; AN, Gary; SAUT, Oliver; GENIN, Guy M.; LUEBECK, E. Georg; POPEL, Aleksander S.; RIBBA, Benjamin; VICINI, Paolo; ZHOU, Xiaobo; WEIS, Jared A.; YE, Kaiming

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YANKEELOV, Thomas E.
University of Texas at Austin [Austin]

AN, Gary
University of Chicago

SAUT, Oliver
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]

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Ce document a été publié dans

Annals of Biomedical Engineering. 2016-07-06, vol. 44, n° 9

Springer Verlag

Résumé en anglais

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 oncol- ogy. 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 illustrat- ing their application as well as the current gap between pre- clinical and clinical applications. We conclude with a discus- sion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.< Réduire

Mots clés en anglais

Mathematical modeling

Cancer

Cancer screening

Computational modeling

Numerical modeling

Agent-based modeling

Predictive oncology

Epidemiology

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

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