Core 1: Data Management and Computational Support Core

核心1：数据管理和计算支持核心

• 批准号: 10270309
• 负责人: Uwe Titt
• 金额: $ 26.59万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10270309
• 关键词: Address; Back; Biological; Biological Factors; Biostatistics Core; Clinical; Clinical Treatment; Collaborations; Collection; Computer software; Correlative Study; Data; Data Collection; Data Security; Data Set; Data Storage and Retrieval; Database Management Systems; Databases; Decision Making; Dependence; Digital Imaging and Communications in Medicine; Dose; Effectiveness; Elements; Enrollment; Ensure; Excision; Experimental Designs; Extramural Activities; Funding; Goals; Grant; Image; In Vitro; Individual; Infrastructure; Institution; Intervention; Intuition; Knowledge; Laboratory Study; Legal; Linear Energy Transfer; Link; Maintenance; Manuals; Monte Carlo Method; Names; Nomenclature; Organ; Outcome; Outcomes Research; Patients; Performance; Photons; Process; Program Research Project Grants; Protons; Radiation Dose Unit; Radiation exposure; Radiation therapy; Randomized; Relative Biological Effectiveness; Research Personnel; Risk; Safety; Scheme; Services; Standardization; Structure; System; Techniques; Testing; Time; United States National Institutes of Health; University of Texas M D Anderson Cancer Center; access restrictions; data exchange; data integrity; data management; data sharing; design; dosimetry; experimental study; follow-up; immunoregulation; improved; in vivo; insight; inter-institutional; interest; irradiation; novel; pre-clinical research; prospective; quality assurance; radiomics; repository; research and development; simulation; structured data; tool; treatment planning

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.

核心1：预防性-项目总结 核心1旨在解决开发临床和剂量测定数据存储的重要任务， 规划、交付和跟进患者治疗。传统上，数据通常不记录在井中- 结构化和标准化的方式。例子包括使用不一致的结构名称;多重处理 计划和缺乏明确性，即哪些计划实际用于治疗，哪些计划仅在计划期间使用 优化;以及不完整的数据，例如缺失待研究的风险器官的轮廓。用于可靠 相关的研究，至关重要的是，策展过程消除这些缺陷。这些任务往往 手动尝试我们建议用自动化技术补充手动流程， 并在可能的情况下自动纠正这些问题或标记它们以进行手动干预。 虽然大量的治疗计划，临床结果和研究相关的数据集已经 由于数据的产生，获取数据常常受到上述技术问题的阻碍， 由机构间数据共享的法律的问题和一般数据安全问题引起。此外，数据 在不同机构收集的数据可能有不同的要素和格式，需要加以协调。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，但还需要作出重大努力， 有必要实施并将其集成到基础设施中，并维护这些基础设施，以确保高效 安全的数据管理。必须开发、测试和实施新的工具，以供国际社会使用。 机构数据交换和管理。 除了对已经积累的数据进行管理之外，还需要准确的剂量和LET分布， 项目中提出的生物学研究。这些实验可以使用Monte Carlo方法进行前瞻性设计。 为了积极探索生物学和免疫调节效应对 剂量和LET。