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

SCH：头部适应性放射治疗的个性化重新安排

• 批准号: 10737817
• 负责人: Clifton David Fuller
• 金额: $ 8.39万
• 依托单位: RICE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10737817
• 关键词: Address; Adoption; Affect; Aging; Algorithms; Anatomy; Area; Behavior; Cancer Control; Capital; Clinical; Clinical Data; Clinical Treatment; Communities; Community Hospitals; Complication; Computer software; Coupled; Data; Data Set; Decision Making; Decision Support Systems; Development; Devices; Diagnosis; Dose; Economics; Emerging Technologies; Environment; Equipment; Evaluation; Evolution; Goals; Head Cancer; Head and Neck Cancer; Head and neck structure; Health; Health Policy; Health Technology; Healthcare; Human; Human Resources; Image; Incentives; Individual; Instruction; Insurance Carriers; Intervention; Life; Linear Accelerator Radiotherapy Systems; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of larynx; Malignant neoplasm of pharynx; Medical Device; Medicare; Methodology; Methods; Modeling; Modernization; Monitor; Morbidity - disease rate; Neck Cancer; Normal tissue morphology; Operations Research; Organ; Outcome; Overdose; Patient Care; Patient imaging; Patients; Physicians; Policies; Probability; Process; Property; Provider; Radiation; Radiation therapy; Regimen; Research; Resources; Risk; SMART health; Schedule; Science; Self-Help Devices; Series; Software Tools; Structure; Survivors; System; Systems Integration; Techniques; Technology; Time; Toxic effect; Tumor Volume; Uncertain Risk; Uncertainty; Validation; Variant; Work; advanced analytics; base; cancer radiation therapy; chemoradiation; clinical implementation; connected health; cost; design; evidence base; head and neck cancer patient; image guided radiation therapy; improved; in silico; individual patient; individualized medicine; innovation; insight; low and middle-income countries; malignant mouth neoplasm; models and simulation; multidisciplinary; new technology; next generation; novel; programs; prototype; radiation-induced injury; response; risk stratification; side effect; soft tissue; standard care; support tools; theories; tool; treatment planning; treatment response; treatment stratification; tumor

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.

头颈癌（HNC）占美国所有癌症和大多数癌症的近3％ 通常会影响衰老的个体。虽然化学疗法是标准治疗 HNC的方法，已知该方法会引起大量的副作用。尤其， 治疗期间发生的解剖变化可能导致临床覆盖不足 有风险的器官的目标量或过度剂量。 该项目将开发自适应放射疗法（ART）的新型优化模型 - 通过评估的单个患者的定制治疗计划方法 肿瘤反应的系统和随机变化。这些模型将使用肿瘤的成像数据 数量和正常组织并发症的可能性，以确定最佳数量和时机 治疗补充。模型将通过顺序提供个性化优化 基于对治疗的反应以及优化和评估的决策 简单的阈值重建政策经常被医生使用。面对最佳行为 还将使用相互矛盾的付款人/提供者的观点来分析新兴技术的观点 机理设计技术。艺术需要有关患者国家和过渡的信息 概率描述了肿瘤的进化。由于该过程固有地要求 在不确定性下进行的顺序决策，提出的优化模型使用马尔可夫 决策过程（MDP）。在实践中可能难以实施最佳的最佳政策， 特别是在缺乏最先进设备的中心中。因此，拟议的工作将进一步 使用双重编程框架评估简单的阈值重新义策略。在 尤其是，双重计划将通过最小化各种患者类别的阈值。 与MDP规定的政策背道而驰。 提出的框架为方法论贡献提供了多种途径。小说 MDP设计框架是一种非常强大的工具，可用于建模许多工具 有趣的问题。我们将探索离散连续状态空间和 根据患者成像数据估计过渡可能性。我们将探索算法 解决双层程序，特别是利用低级MDP的结构和特性 型号。最后，我们将研究经济学的主要代理框架的应用 建模用于新兴临床疗法的付款人/提供者的互动。 相关性（请参阅说明）： 这项研究使用新型模型来开发和验证一种减少癌症的综合方法 放疗副作用，同时维持或改善癌症控制。它将最大化效率 病人和提供者。它的发现将为您的个人辐射计划的决定提供信息，优化风险 - 分层的治疗和医疗保健政策实施有效的新技术干预措施。