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

头颈部癌症(HNC)占美国所有癌症的近3%，而且大多数 通常影响到年老的人。而化疗和放射治疗是标准的治疗方法 对于HNC的方法，该方法已知会引起巨大的副作用。特别是， 在治疗过程中发生的解剖变化可能会导致临床报道不足。 危险器官的靶量或过量。 该项目将为自适应放射治疗(ART)开发新的优化模型 为个别患者设计的个性化治疗计划方法，通过评估 肿瘤反应的系统性和随机性变化。这些模型将使用肿瘤的成像数据 确定最佳数量和时机的体积和正常组织并发症概率 重新制定治疗计划。这些模型将通过顺序提供个性化的优化 以治疗反应为基础的决策，以及对 医生经常使用的简单门槛重新规划政策。面对最优行为 还将使用以下工具分析新兴技术的支付方/提供商观点冲突 机械设计技术。ART需要有关患者状态和转换的信息 描述肿瘤随时间演变的概率。因为该过程本身就需要 不确定条件下的序贯决策问题，提出了基于马尔可夫模型的优化模型 决策过程(MDP)。所得到的最优策略可能难以在实践中实现， 尤其是在缺乏最先进设备的中心。因此，拟议的工作将进一步 使用双层规划框架评估简单的门槛重新规划政策。在……里面 具体地说，双层程序将通过最小化为各种患者类别找到阈值 偏离MDP规定的政策。 拟议的框架为方法学贡献提供了多种途径。这部小说 MDP设计框架是一个令人难以置信的强大工具，可以用来对许多 有趣的问题。我们将探索离散化连续状态空间的方法，并 基于患者成像数据估计转移概率。我们将探索以下算法： 求解两层规划，特别是利用低层MDP的结构和性质 模特们。最后，我们将从经济学的角度研究委托代理框架的应用。 为新出现的临床疗法建立付款人/提供者互动模型。 相关性(请参阅说明)： 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。 放射治疗的副作用，同时维持或改善癌症控制。它将最大限度地提高 患者和提供者。它的发现将为个人放射计划的决策提供依据，优化风险- 分层治疗，实施有效的新技术干预的医疗保健政策。

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.