Core 1: Data Management and Computational Support Core

核心1：计算--项目摘要 核心1旨在解决在以下时间开发临床和剂量数据存储的重要任务 患者治疗的计划、交付和跟踪。传统上，数据通常不会记录在油井中- 结构化、标准化的方式。例如，使用不一致的结构名称；多重处理 计划和不清楚哪个计划(S)实际用于治疗，而不是简单地在计划期间使用 优化；以及不完整的数据，例如丢失了要研究的有风险的器官的轮廓。为了可靠 在相关研究中，有必要通过一个管理过程来消除这些缺陷。这样的任务通常是 手动尝试。我们建议用自动化技术来补充人工过程，以识别 特定问题，并在可能的情况下自动纠正，或将其标记为手动干预。 虽然大量的治疗计划、临床结果和研究相关的数据集已经 在产生数据时，访问数据往往会受到上述技术问题和问题的阻碍 产生于机构间数据共享的法律问题和一般数据安全问题。此外，数据 在不同机构捕获的数据可能具有需要协调的不同元素和格式。许多 软件和基础设施解决方案已经可以用来克服这些障碍，但大量的努力将 有必要实施它们并将其集成到基础设施中，并对其进行维护以确保高效 和安全的数据管理。必须开发、测试和实施新的工具，以实现 机构数据交换和管理。 除了对已积累的数据进行整理外，还需要准确的剂量和LET分布 项目中提出的生物学研究。这些实验可能会使用蒙特卡罗方法进行前瞻性设计 卡罗模拟，以积极探索生物和免疫调节效应的依赖性 剂量和LET都是如此。

CORE 1: COMPUTATIONAL - PROJECT SUMMARY Core 1 aims to address the important task of developing clinical and dosimetry data storage at the time of planning, delivery and follow up of patient treatments. Traditionally, data is typically not recorded in a well- structured and standardized way. Examples include the use of inconsistent structure names; multiple treatment plans and lack of clarity as to which plan(s) were actually used to treat vs. used simply during the plan optimization; and incomplete data, for instance missing contours of an organ at risk to be studied. For reliable correlative studies, it is essential that a curation process removes such deficiencies. Such tasks are often attempted manually. We propose to supplement manual processes with automated techniques to identify specific problems and rectify them automatically where possible or flag them for manual intervention. While large sets of treatment planning, clinical outcomes and research related data sets have already been produced, access to the data is often obstructed by technical problems mentioned above and also by problems arising from legal issues of inter-institutional data sharing and general data safety issues. In addition, data captured at different institutions may have different elements and formats that need to be reconciled. Many software and infrastructure solutions are already available to overcome such hurdles, but significant effort will be necessary to implement and integrate them into an infrastructure and to maintain these to ensure efficient and safe data management. Novel tools will have to be developed, tested and implemented, for inter- institutional data exchange and management. In addition to the curation of already accumulated data, accurate dose and LET distributions are necessary for the biological studies proposed in the projects. These experiments may be prospectively designed using Monte Carlo simulations in order to actively explore the dependence of biological and immunomodulatory effects on both dose and LET.