Decision support for dose prescription in radiation treatment planning

描述（由申请人提供）：放射治疗[1]的最新进展，如强度调制放疗（IMRT）和图像引导放疗（IGRT），提供了最大限度地控制肿瘤的能力，同时降低了辐射引起的对邻近正常组织损伤的风险。通常，放射治疗包括三个阶段：(1)处方-放射肿瘤学家（医生）指定目标和危险器官（OAR）的剂量限制；(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——治疗师执行治疗病人的计划。在目前的实践中，放射肿瘤学家通常利用各种来源进行剂量处方，包括1991年关于正常组织耐受性的“Emami”论文b[8]， QUANTEC的更新指南，期刊和文本中的其他数据以及他们的个人经验。虽然这些提供了正常组织并发症对剂量分布的依赖或患者群体中器官耐受上限的一般理解，但它们对个体患者的应用不太确定和精确。文献中可用的数据和指南的应用进一步复杂化，因为这些信息只能作为叙述性文本、表格和图表获得，难以定量地整合到临床实践中。此外，现有的指南没有考虑个别治疗可达到的理想剂量分布的患者具体信息[10]。放射肿瘤学家经常被迫通过综合现有的人群水平指南、个人经验和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、计划人员和治疗师提供基于证据的决策支持来改善结果。在这个项目中，我们建议开发实用和临床有用的决策支持工具，以帮助放射肿瘤学家规定患者特定的最佳剂量限制。具体目标是：(1)为放射肿瘤学家提供可靠的预测，根据患者的解剖结构和肿瘤体积实现患者特异性剂量分布；(2)为放射肿瘤学家提供直观的工具，将患者特异性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，本项目开发的技术不仅可以提高放疗处方的质量，还可以减少最佳剂量约束下的计划时间，改善临床效果。

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.