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

Overview

Journal Ann Biomed Eng

Specialty Biomedical Engineering

Date 2016 Jul 8

PMID 27384942

Citations 34

Authors

Thomas E Yankeelov

Gary An

Oliver Saut

E Georg Luebeck

Aleksander S Popel

Benjamin Ribba

Paolo Vicini

Xiaobo Zhou

Jared A Weis

Kaiming Ye

Guy M Genin

Affiliations

Soon will be listed here.

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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