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.

癌症进展的复杂性和可变性需要一个用于治疗决策的定量范式，该范式是动态的、个性化的，并且能够在巨大的不确定性下为个体患者确定最佳治疗策略。在这里，我们讨论了这种方法的核心组成部分和挑战，并强调在其开发过程中需要进行全面的纵向临床和分子数据集成。我们描述了数学建模和机器学习在构建动态最佳癌症治疗策略中的互补和不同作用，并强调了强化学习方法在这一努力中的潜力。