精密医学
医学
医学物理学
临床肿瘤学
临床试验
梅德林
作者
Dalit Engelhardt,Franziska Michor
标识
DOI:10.1016/j.trecan.2021.01.006
摘要
The complexity and variability of cancer progression necessitate a quantitative paradigm for therapeutic decision-making that is dynamic, personalized, and capable of identifying optimal treatment strategies for individual patients under substantial uncertainty. Here, we discuss the core components and challenges of such an approach and highlight the need for comprehensive longitudinal clinical and molecular data integration in its development. We describe the complementary and varied roles of mathematical modeling and machine learning in constructing dynamic optimal cancer treatment strategies and highlight the potential of reinforcement learning approaches in this endeavor.
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