Wang Liwei, Wen Andrew, Fu Sunyang, Ruan Xiaoyang, Huang Ming, Li Rui, Lu Qiuhao, Lyu Heather, Williams Andrew E, Liu Hongfang
McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Department of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
NPJ Digit Med. 2025 Apr 7;8(1):189. doi: 10.1038/s41746-025-01581-7.
The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) supports large-scale research by enabling distributed network analyses. However, the breadth of its adoption in cancer research is not well understood. We conducted a scoping review to describe the adoption of the OMOP CDM in cancer research. A total of 49 unique articles were included in the review, with 30 on the data analysis theme, and 20 on the infrastructure theme. This review highlighted that while the OMOP CDM ecosystem has enabled successful data support for cancer research, particularly for collaborative studies, ongoing model development and iterative improvement remain needed to fulfill additional research data needs. Expanding disease sites, specifically for rare cancers, integrating more diverse types of data sources, improving data quality, adopting advanced analytics methodology, and increasing multisite evaluations serve as important opportunities to facilitate secondary usage of observational data in future cancer research.
观察性医学结局合作组织(OMOP)通用数据模型(CDM)通过实现分布式网络分析来支持大规模研究。然而,其在癌症研究中的应用广度尚未得到充分了解。我们进行了一项范围综述,以描述OMOP CDM在癌症研究中的应用情况。该综述共纳入49篇独特的文章,其中30篇关于数据分析主题,20篇关于基础设施主题。本综述强调,虽然OMOP CDM生态系统已为癌症研究,特别是协作研究提供了成功的数据支持,但仍需要持续的模型开发和迭代改进,以满足更多的研究数据需求。扩大疾病部位,特别是针对罕见癌症,整合更多样化类型的数据源,提高数据质量,采用先进的分析方法,以及增加多中心评估,是促进未来癌症研究中观察性数据二次利用的重要机遇。