Call for Data Standardization: Lessons Learned and Recommendations in an Imaging Study.
Call for Data Standardization: Lessons Learned and Recommendations in an Imaging Study.
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DOI：
10.1200/cci.19.00056
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发表时间：
2019-11-01
影响因子：
4.2
通讯作者：
Jacobs, Paula
中科院分区：
文献类型：
--
作者：
Basu, Amrita;Warzel, Denise;Jacobs, Paula
目得：数据共享可以节省潜在的成本，支持数据聚合，并促进可重复性，以确保研究质量;然而，来自异构系统的数据需要追溯性协调。这是寻求利用现有数据的研究人员的主要障碍。致力于数据互操作性策略的努力主要集中在标准的使用上，但忽略了竞争标准的问题和现有数据的价值。互操作性仍然依赖于追溯性协调。方法：癌症影像档案（TCIA）是一个例子，接受来自不同来源的数据的成像库，以减少这种负担是needed.METHODS。它包含来自世界各地的研究者的医学图像和大量的非图像数据。医学数字成像和通信（DICOM）标准允许跨图像查询，但TCIA不强制执行描述非图像支持数据（如治疗细节和患者结局）的其他标准。在这项研究中，我们使用了9个TCIA肺和脑非图像文件，其中包含659个字段，以探索跨研究查询和汇总的回顾性协调。花了329.5小时（即2.3个月）（延长了6个月）来识别3个或更多文件中的41个重叠字段并转换其中的31个。我们使用的基因组数据共享（GDC）的数据元素作为目标标准的harmonization.RESULTS：我们的特点的问题，并制定了建议，减少回顾性协调的负担。一旦我们协调的数据，我们还开发了一个Web工具，方便地探索协调collections. CONCLUSION：虽然预期使用的标准可以支持互操作性，有问题，使这一目标复杂化。我们的工作认识到并揭示了在尝试重复使用现有数据时的追溯性协调问题，并建议国家基础设施来解决这些问题。
PURPOSE: Data sharing creates potential cost savings, supports data aggregation, and facilitates reproducibility to ensure quality research; however, data from heterogeneous systems require retrospective harmonization. This is a major hurdle for researchers who seek to leverage existing data. Efforts focused on strategies for data interoperability largely center around the use of standards but ignore the problems of competing standards and the value of existing data. Interoperability remains reliant on retrospective harmonization. Approaches to reduce this burden are needed.METHODS: The Cancer Imaging Archive (TCIA) is an example of an imaging repository that accepts data from a diversity of sources. It contains medical images from investigators worldwide and substantial nonimage data. Digital Imaging and Communications in Medicine (DICOM) standards enable querying across images, but TCIA does not enforce other standards for describing nonimage supporting data, such as treatment details and patient outcomes. In this study, we used 9 TCIA lung and brain nonimage files containing 659 fields to explore retrospective harmonization for cross-study query and aggregation. It took 329.5 hours, or 2.3 months, extended over 6 months to identify 41 overlapping fields in 3 or more files and transform 31 of them. We used the Genomic Data Commons (GDC) data elements as the target standards for harmonization.RESULTS: We characterized the issues and have developed recommendations for reducing the burden of retrospective harmonization. Once we harmonized the data, we also developed a Web tool to easily explore harmonized collections.CONCLUSION: While prospective use of standards can support interoperability, there are issues that complicate this goal. Our work recognizes and reveals retrospective harmonization issues when trying to reuse existing data and recommends national infrastructure to address these issues.