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在结构化存储库中整合生物学和放射学数据：应用于 COSMOS 案例研究的数据模型。

Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study.

机构信息

Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy.

Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy.

出版信息

J Digit Imaging. 2022 Aug;35(4):970-982. doi: 10.1007/s10278-022-00615-w. Epub 2022 Mar 16.

DOI:10.1007/s10278-022-00615-w
PMID:35296941
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9485502/
Abstract

Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.

摘要

将生物样本信息与数字数据（如医学图像）整合，随着精准医学的出现而受到关注。该领域的研究面临着越来越多的数据需要管理，因此需要以功能和标准化的方式对这些数据进行结构化，以促进机构之间的合作。受生物库数据共享最小信息（MIABIS）的启发，我们提出了一个扩展的数据模型，旨在标准化涉及生物和数字样本的数据集。在所提出的模型中，非常强调因素之间的因果关系，因为这些关系在临床工作流程中经常遇到。为了在现实环境中测试数据模型，我们考虑了连续观察吸烟受试者（COSMOS）数据集作为案例研究，该数据集包含超过 5000 名受试者的连续 10 年肺癌筛查和随访。还介绍了 COSMOS 数据库的结构，该数据库的实现旨在方便数据检索过程，以及我们希望在肺癌筛查研究公共存储库中共享的数据描述。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/8fd017badffc/10278_2022_615_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/7c1402c03237/10278_2022_615_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/8de3783b730d/10278_2022_615_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/a4ca0fa7ffea/10278_2022_615_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/ddb76f04328d/10278_2022_615_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/8fd017badffc/10278_2022_615_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/7c1402c03237/10278_2022_615_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/8de3783b730d/10278_2022_615_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/a4ca0fa7ffea/10278_2022_615_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/ddb76f04328d/10278_2022_615_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3a/9485502/8fd017badffc/10278_2022_615_Fig5_HTML.jpg

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本文引用的文献

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Phys Med. 2021 Oct;90:23-29. doi: 10.1016/j.ejmp.2021.08.011. Epub 2021 Sep 13.
2
Artificial Intelligence for the Future Radiology Diagnostic Service.面向未来放射诊断服务的人工智能
Front Mol Biosci. 2021 Jan 28;7:614258. doi: 10.3389/fmolb.2020.614258. eCollection 2020.
3
Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review.
系统医学术语命名法（SNOMED CT）在医疗保健中处理自由文本的应用：系统范围综述。
J Med Internet Res. 2021 Jan 26;23(1):e24594. doi: 10.2196/24594.
4
Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training.主动构建用于人工智能培训的标注成像数据库。
J Digit Imaging. 2021 Feb;34(1):105-115. doi: 10.1007/s10278-020-00384-4.
5
Key insights in the AIDA community policy on sharing of clinical imaging data for research in Sweden.瑞典 AIDA 社区关于为研究分享临床影像数据的政策中的主要观点。
Sci Data. 2020 Oct 6;7(1):331. doi: 10.1038/s41597-020-00674-0.
6
Causality matters in medical imaging.医学影像学中因果关系很重要。
Nat Commun. 2020 Jul 22;11(1):3673. doi: 10.1038/s41467-020-17478-w.
7
External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis.基于影像组学的预测模型在低剂量CT筛查早期肺癌诊断中的外部验证
Med Phys. 2020 Sep;47(9):4125-4136. doi: 10.1002/mp.14308. Epub 2020 Jun 23.
8
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Biopreserv Biobank. 2020 Jun;18(3):155-164. doi: 10.1089/bio.2019.0129. Epub 2020 Apr 17.
9
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Br J Radiol. 2020 Feb 1;93(1106):20190855. doi: 10.1259/bjr.20190855.
10
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Eur J Nucl Med Mol Imaging. 2019 Dec;46(13):2722-2730. doi: 10.1007/s00259-019-04382-9. Epub 2019 Jun 15.