背景:观察性医学结果伙伴关系-通用数据模型(OMOP-CDM),分布式研究网络,临床数据覆盖率低。放射学数据很有价值,但是成像元数据通常是不完整的,OMOP-CDM中缺乏标准化的记录格式。我们为放射学_CDM(R_CDM)开发了基于Web的管理系统和数据质量评估(RQA)工具,并评估了临床应用该数据集的可行性。
方法:我们设计了具有放射学_发生和放射学_图像表的R_CDM。这与OMOP-CDM临床数据无缝关联。我们使用RadLex剧本采用了标准化术语,并将5,753个放射学协议术语映射到OMOP词汇。摘录,变换,和加载(ETL)过程的开发是为了提取难以从元数据中提取的详细信息,并补偿缺失的值。进行基于图像的定量以测量肝脏表面结节(LSN),使用Wonkwang定制腹部和肝脏总溶液(WALTS)软件。
结果:在PACS上,368,333,676个DICOM文件(1,001,797例)转换为R_CDM慢性肝病(CLD)数据(316,596个MR图像,228例;926,753CT图像,782起案件),并上传到基于Web的管理系统。采集日期和分辨率被准确提取,但是其他信息,如“对比管理状态”和“摄影方向”,无法从元数据中提取。使用WALTS,9,609例对比前轴平面腹部MR图像(197例CLD病例)按METAVIR纤维化等级分配LSN评分,方差分析有显著差异(p<0.001)。平均RQA评分(83.5)表明质量良好。
结论:这项研究开发了一个基于Web的系统来管理R_CDM数据集,RQA工具,并构建了一个CLDR_CDM数据集,具有良好的临床应用质量。我们的管理系统和R_CDMCLD数据集将有助于多中心和基于图像的量化研究。
BACKGROUND: The Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM), a distributed research network, has low clinical data coverage. Radiological data are valuable, but imaging metadata are often incomplete, and a standardized recording format in the OMOP-CDM is lacking. We developed a web-based management system and data quality assessment (RQA) tool for a radiology_CDM (R_CDM) and evaluated the feasibility of clinically applying this dataset.
METHODS: We designed an R_CDM with Radiology_Occurrence and Radiology_Image tables. This was seamlessly linked to the OMOP-CDM clinical data. We adopted the standardized terminology using the RadLex playbook and mapped 5,753 radiology protocol terms to the OMOP vocabulary. An extract, transform, and load (ETL) process was developed to extract detailed information that was difficult to extract from metadata and to compensate for missing values. Image-based quantification was performed to measure liver surface nodularity (LSN), using customized Wonkwang abdomen and liver total solution (WALTS) software.
RESULTS: On a PACS, 368,333,676 DICOM files (1,001,797 cases) were converted to R_CDM chronic liver disease (CLD) data (316,596 MR images, 228 cases; 926,753 CT images, 782 cases) and uploaded to a web-based management system. Acquisition date and resolution were extracted accurately, but other information, such as \"contrast administration status\" and \"photography direction\", could not be extracted from the metadata. Using WALTS, 9,609 pre-contrast axial-plane abdominal MR images (197 CLD cases) were assigned LSN scores by METAVIR fibrosis grades, which differed significantly by ANOVA (p < 0.001). The mean RQA score (83.5) indicated good quality.
CONCLUSIONS: This study developed a web-based system for management of the R_CDM dataset, RQA tool, and constructed a CLD R_CDM dataset, with good quality for clinical application. Our management system and R_CDM CLD dataset would be useful for multicentric and image-based quantification researches.