Developing knowledge models to enable rapid learning in radiation therapy

开发知识模型以实现放射治疗的快速学习

• 批准号: 9282771
• 负责人: Yaorong Ge
• 金额: $ 44.05万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9282771
• 关键词: Address; Adverse effects; Area; Cancer Patient; Caring; Cause of Death; Clinic; Clinical; Clinical Data; Clinical Research; Clinical Trials; Complex; Data; Data Set; Dose; Effectiveness; Ensure; Equipment; Failure; Guidelines; Head and neck structure; Healthcare; Healthcare Systems; Informatics; Institute of Medicine (U.S.); Intensity-Modulated Radiotherapy; Intuition; Knowledge; Lead; Learning; Lung; Malignant Neoplasms; Methods; Modality; Modeling; Multi-Institutional Clinical Trial; Organ; Outcome; Patient Care; Patients; Performance; Physicians; Procedures; Prostate; Protocols documentation; Quality of life; Radiation; Radiation therapy; Research; Resources; Risk; Statistical Methods; Study models; Survivors; System; Technology; Translating; Treatment outcome; United States; Variant; Vision; Work; base; cancer care; cancer site; cancer therapy; cancer type; clinical practice; clinically relevant; cohort; cost; data integration; design; evidence base; experience; health care delivery; improved; individual patient; insight; laboratory experiment; learning progression; learning strategy; method development; outcome forecast; radiation effect; success

Abstract The present proposal aims to develop a rapid learning system for radiation therapy that can provide evidence based, patient-specific treatment guidance for physicians. Rapid-learning health care is a vision proposed by Institute of Medicine to transform the health care delivery into one which generates and applies “as rapidly as possible the evidence needed to deliver the best care for each cancer patient”. Radiation therapy (RT) is a cancer treatment modality that applies complex radiation delivery equipment to deliver highly conformal dose distribution with minimized damage to organs-at-risk (OARs). Because of the complexity of technologies, the incomplete understanding of radiation effects, and the variability of patients and patient conditions, significant improvements in RT effectiveness can come from learning to use the current RT technologies optimally. In the past a few years, our group and a number of other research groups have developed IMRT dose prediction and planning models using routine clinical plan data that produced encouraging results in learning planning knowledge and improving plan quality. These efforts represent early successes in the first aspect of a rapid learning system. However, these existing efforts have mostly focused on a few major cancer sites and are limited to the “learning” aspect. Substantial further work on expanding the models, translating the models into clinical practice, and closing the loop for continuous learning is required to truly enable rapid learning in radiation therapy. The present proposal aims to develop a comprehensive and integrated set of models and methods that will enable rapid learning in radiation therapy with the following specific aims: (1) Develop and enhance IMRT planning models to cover all major cancer sites and treatment scenarios; (2) Translate the models into clinical practice to provide best-achievable patient-specific RT planning and enable continuous improvement of the models via incremental learning; (3) Validate the knowledge models and assess the performance and value of the rapid learning framework. While this project will focus on rapid learning of the planning aspect of radiation therapy, we anticipate that the same framework can be extended to incorporate outcomes data and ultimately lead to a complete rapid learning framework that leads to continuously improved quality of cancer care at lower cost.

摘要 目前的建议旨在开发一个快速学习系统的放射治疗，可以提供证据， 为医生提供基于患者的治疗指导。快速学习医疗保健是一个愿景， 医学研究所将医疗保健服务转变为一个产生和应用“尽快， 为每位癌症患者提供最佳护理所需的证据”。 放射治疗（RT）是一种癌症治疗方式，其应用复杂的放射递送设备来治疗癌症。 提供高度适形的剂量分布，对危及器官（OAR）的损伤最小化。因为 技术的复杂性、对辐射影响的不完全理解以及患者的可变性， 患者的情况下，RT有效性的显着改善可以来自学习使用当前的RT 技术优化。 在过去的几年里，我们的小组和其他一些研究小组已经开发了IMRT剂量 使用常规临床计划数据的预测和规划模型，在学习中产生了令人鼓舞的结果 规划知识，提高规划质量。这些努力代表了在第一个方面取得的初步成功， 快速学习系统然而，这些现有的努力主要集中在几个主要的癌症部位， 仅限于“学习”方面。在扩展模型、翻译模型方面进一步开展大量工作 进入临床实践，并关闭持续学习的循环，需要真正实现快速学习， 放射治疗本提案旨在制定一套全面综合的模式， 方法，这将使快速学习放射治疗与以下具体目标：（1）发展和 增强IMRT计划模型，以覆盖所有主要癌症部位和治疗方案;（2）将IMRT计划模型翻译为 将模型应用于临床实践，以提供最佳的患者特定RT计划， 通过增量学习改进模型;（3）验证知识模型并评估 快速学习框架的绩效和价值。 虽然这个项目将侧重于快速学习放射治疗的规划方面，我们预计， 同样的框架可以扩展到纳入结果数据，并最终导致一个完整的快速 学习框架，以更低的成本不断提高癌症护理的质量。