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：计算 - 项目摘要 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、实施和跟踪患者治疗。传统上，数据通常不会记录在井中 结构化和标准化的方式。示例包括使用不一致的结构名称；多次治疗 缺乏明确的计划，哪些计划实际用于治疗，哪些计划仅在计划期间使用 优化;数据不完整，例如缺少待研究风险器官的轮廓。为了可靠 相关研究表明，管理过程消除此类缺陷至关重要。此类任务经常 手动尝试。我们建议用自动化技术来补充手动流程来识别 特定问题并在可能的情况下自动纠正它们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数据集已经被 产生后，对数据的访问常常受到上述技术问题和问题的阻碍 由机构间数据共享的法律问题和一般数据安全问题引起。此外，数据 不同机构捕获的数据可能具有不同的要素和格式，需要进行协调。许多 软件和基础设施解决方案已经可以克服这些障碍，但还需要付出巨大的努力 有必要实施并将它们集成到基础设施中并维护它们以确保高效 和安全的数据管理。必须开发、测试和实施新的工具，以便 机构数据交换和管理。 除了整理已经积累的数据外，准确的剂量和 LET 分布对于 项目中提出的生物学研究。这些实验可以使用 Monte 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对 剂量和 LET。