权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Decision support for dose prescription in radiation treatment planning

放射治疗计划中剂量处方的决策支持

基本信息

批准号：
8507627
负责人：
Yaorong Ge
金额：
$ 15.83万
依托单位：
UNIVERSITY OF NORTH CAROLINA CHARLOTTE
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-01-01 至 2015-06-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Recent advances in radiation therapy [1], such as Intensity Modulated Radiotherapy (IMRT) and Image-Guided Radiotherapy (IGRT), offer the ability to maximize tumor control while reducing the risk of radiation-induced damage to adjacent normal tissue. Typically, radiation therapy involves three phases: (1) prescription - where radiation oncologists (physicians) specify the dose constraints for targets and organs at risk (OAR); (2) planning - where treatment planners (physicists, dosimetrists) determine the treatment parameters to achieve the prescribed dose constraints; and (3) treatment - where therapists carry out the plan to treat the patients. In current practice, radiation oncologists typically draw on a variety of sources for dose prescription, including the 1991 "Emami" paper [8] on normal tissue tolerance, updated guidance from QUANTEC, other data in journals and texts, and their personal experiences. While these provide a general understanding of the dependence of normal tissue complication on dose distribution or the upper limits of the organ tolerance in populations of patients, their application to an individual patient is less certain and precise. Application of data and guidelines that are available in the literature is further complicated by the fact that this information is available only as narrative texts, tables and charts that are difficult to quantitatively integrate into clinical practice. Furthermore, the existing guidelines do not consider patient specific information regarding the ideal dose distribution achievable at individual treatments [9]. Radiation oncologists are frequently forced to make difficult prescription decisions by synthesizing available population level guidelines, personal experience, and their understanding of the specific patient needs on an ad hoc basis. Our overarching goal is to improve outcome by providing evidence-based decision support for radiation oncologists, planners, and therapists in every phase of the treatment process. In this project we propose to develop practical and clinically useful decision support tools to help radiation oncologists prescribe patient- specific optimal dose constraints. The specific aims are (1) Provide radiation oncologists with reliable predictions of patient-specific dose distributions achievable for the patient's anatomy and tumor volume; and (2) Provide radiation oncologists with intuitive tools that integrate patient-specific dose predictions with population-based dose guidelines to support prescription decision making. We believe the technologies developed in this project will not only improve the quality of radiotherapy prescriptions but also reduce planning time with optimal dose constraints and improve clinical outcomes.

描述(申请人提供)：放射治疗的最新进展[1]，如调强放射治疗(IMRT)和图像引导放射治疗(IGRT)，提供了最大限度地控制肿瘤的能力，同时降低了辐射对邻近正常组织造成损害的风险。通常，放射治疗包括三个阶段：(1)处方--放射肿瘤学家(医生)为目标和危险器官(OAR)指定剂量限制；(2)计划--治疗计划者(物理学家、剂量师)确定治疗参数以达到规定的剂量限制；(3)治疗--治疗师执行治疗计划来治疗患者。在目前的实践中，放射肿瘤学家通常利用各种来源的剂量处方，包括1991年关于正常组织耐受性的“Emami”论文[8]、来自QUANTEC的最新指南、期刊和文本中的其他数据以及他们的个人经验。虽然这些方法提供了对正常组织并发症对患者群体中剂量分布或器官耐受性上限的依赖的一般理解，但它们在单个患者中的应用不那么确定和准确。文献中可获得的数据和指南的应用因以下事实而进一步复杂化，即这些信息仅作为叙述性文本、表格和图表可用，难以量化地整合到临床实践中。此外，现有的指南没有考虑患者的特定信息，即个体治疗可实现的理想剂量分布[9]。放射肿瘤学家经常被迫通过综合现有的人群水平指南、个人经验以及他们对特定患者需求的理解来做出困难的处方决定。我们的首要目标是通过在治疗过程的每个阶段为放射肿瘤学家、规划师和治疗师提供循证决策支持来改善结果。在这个项目中，我们建议开发实用的和临床上有用的决策支持工具，以帮助放射肿瘤学家开出针对患者的最佳剂量限制。其具体目标是(1)为放射肿瘤学家提供关于患者解剖和肿瘤体积的患者特定剂量分布的可靠预测；以及(2)为放射肿瘤学家提供直观的工具，将患者特定剂量预测与基于人群的剂量指南相结合，以支持处方决策。我们相信，该项目开发的技术不仅将提高放射治疗处方的质量，还将在最佳剂量约束下缩短计划时间，并改善临床结果。