Annals of Biomedical Engineering

, Volume 44, Issue 9, pp 2626–2641

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

  • 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
Multi-Scale Modeling in the Clinic

DOI: 10.1007/s10439-016-1691-6

Cite this article as:
Yankeelov, T.E., An, G., Saut, O. et al. Ann Biomed Eng (2016) 44: 2626. doi:10.1007/s10439-016-1691-6

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

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

Copyright information

© Biomedical Engineering Society 2016

Authors and Affiliations

  • Thomas E. Yankeelov
    • 1
  • Gary An
    • 2
  • Oliver Saut
    • 3
  • E. Georg Luebeck
    • 4
  • Aleksander S. Popel
    • 5
  • Benjamin Ribba
    • 6
  • Paolo Vicini
    • 7
  • Xiaobo Zhou
    • 8
  • Jared A. Weis
    • 9
  • Kaiming Ye
    • 10
  • Guy M. Genin
    • 11
  1. 1.Departments of Biomedical Engineering and Internal Medicine, Institute for Computational and Engineering SciencesCockrell School of Engineering, The University of Texas at AustinAustinUSA
  2. 2.Department of Surgery and Computation InstituteThe University of ChicagoChicagoUSA
  3. 3.Institut de Mathématiques de Bordeaux, Université de Bordeaux and INRIABordeauxFrance
  4. 4.Program in Computational BiologyFred Hutchinson Cancer Research CenterSeattleUSA
  5. 5.Departments of Biomedical Engineering and OncologyJohns Hopkins University School of MedicineBaltimoreUSA
  6. 6.Pharma Research and Early Development, Clinical PharmacologyF. Hoffmann-La Roche LtdBaselSwitzerland
  7. 7.Clinical Pharmacology and DMPKMedImmuneGaithersburgUSA
  8. 8.Center for Bioinformatics and Systems Biology, RadiologyWake Forest University School of MedicineWinston-SalemUSA
  9. 9.Department of Biomedical EngineeringVanderbilt UniversityNashvilleUSA
  10. 10.Department of Biomedical EngineeringWatson School of Engineering and Applied Science, Binghamton University, State University of New YorkBinghamtonUSA
  11. 11.Departments of Mechanical Engineering and Materials Science, and Neurological SurgeryWashington University in St. LouisSt. LouisUSA

Personalised recommendations