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

作者：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

来源：Annals of Biomedical Engineering, 2016, 44(9): 2626-2641.

DOI：10.1007/s10439-016-1691-6

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

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 preclinical 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.

• 出版日期2016-9
• 单位上海生物信息技术研究中心