SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck Cancer

头颈癌（HNC）占美国所有癌症的近3%， 通常影响老年人。而化疗和放疗是标准的治疗方法 对于HNC的方法，已知该方法会引起显著的副作用。特别是， 治疗期间发生的解剖学变化可能导致临床覆盖不足， 目标体积或危险器官的过量剂量。 该项目将为自适应放射治疗（ART）开发新的优化模型- a 为个体患者设计的定制治疗计划方法， 肿瘤反应的系统和随机变化。该模型将使用肿瘤的成像数据 体积和正常组织并发症概率，以确定最佳数量和时间 的治疗。这些模型将通过顺序优化来提供个性化的优化。 基于治疗反应的决策，以及 医生经常使用的简单阈值重新规划策略。最佳行为面对 对于新兴技术，支付方/提供方之间的观点冲突也将使用 机械设计技术ART需要有关患者状态和过渡的信息 描述肿瘤随时间演变的概率。因为这个过程本质上要求 不确定性下的顺序决策，建议的优化模型使用马尔可夫 决策过程（MDP）。由此产生的最优策略可能难以在实践中实现， 特别是在缺乏最先进设备的中心。因此，拟议的工作将进一步 评估简单的阈值重新规划政策使用双层规划框架。在 特别地，双水平程序将通过最小化 对MDP规定政策的偏离。 拟议框架为方法学贡献提供了多种途径。小说 MDP设计框架是一个非常强大的工具，可以用来建模许多 有趣的问题。我们将探索离散化连续状态空间的方法， 基于患者成像数据估计转移概率。我们将探索算法， 求解双层规划，特别是利用下层MDP的结构和性质 模型最后，我们将从经济学的角度来研究委托代理框架在企业内部管理中的应用。 为新兴的临床治疗建模付款人/提供者交互。 相关性（参见说明）： 这项研究使用新的模型来开发和验证减少癌症的综合方法 放疗副作用，同时保持或改善癌症控制。它将最大限度地提高效率， 患者和供应商。它的发现将为个人辐射计划的决策提供信息，优化风险- 分层治疗和医疗保健政策实施有效的新技术干预。

Head and neck cancers (HNCs) account for nearly 3% of all cancers in the U.S. and most commonly affect aging individuals. While chemo-radiotherapy is the standard treatment approach for HNC, the method is known to cause substantial side-effects. In particular, anatomical changes occurring during the treatment may result in under-coverage of the clinical target volume or over-dosage of organs at risk. The project will develop novel optimization models for Adaptive Radiation Therapy (ART) – a customized treatment planning approach for individual patients designed by evaluating the systematic and random variations in tumor response. The models will use imaging data of tumor volume and normal tissue complication probabilities to determine the optimal number and timing of treatment replans. The models will provide personalized optimization through sequential decision-making based on response to treatment, as well as optimization and evaluation of simple threshold replanning policies often used by doctors. Optimal behavior in the face of conflicting payer/provider perspectives for emerging technologies will also be analyzed using mechanism design techniques. ART requires information about patient-state as well as transition probabilities describing the tumor's evolution over time. Since the process inherently calls for sequential decision-making under uncertainty, the proposed optimization models use a Markov Decision Process (MDP). The resulting optimal policies may be difficult to implement in practice, especially in centers lacking state-of-the-art equipment. Therefore, the proposed work will further evaluate simple threshold replanning policies using a bilevel programming framework. In particular, the bilevel program will find threshold values for various patient classes by minimizing the deviation from the MDP-prescribed policy. The proposed framework offers multiple avenues for methodological contributions. The novel MDP design framework is an incredibly powerful tool that can be used to model many interesting questions. We will explore ways of discretizing the continuous state spaces and estimating transition probabilities based on patient imaging data. We will explore algorithms for solving bilevel programs, especially utilizing the structure and properties of the lower-level MDP models. Finally, we will study applications of the principal-agent framework from economics in modeling payer/provider interactions for emerging clinical therapies. RELEVANCE (See instructions): This study uses novel models to develop and validate an integrated approach to reduce cancer radiotherapy side effects while maintaining or improving cancer control. It will maximize efficiency for patients and providers. Its findings will inform decisions about individual radiation planning, optimize risk- stratified treatment, and healthcare policy implementation of effective new technology interventions.