PDF(Pubmed)
Abstract：
BACKGROUND: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge.
OBJECTIVE: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research.
METHODS: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data.
RESULTS: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings.
CONCLUSIONS: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.
OBJECTIVE: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research.
METHODS: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data.
RESULTS: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings.
CONCLUSIONS: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.
摘要：
背景：数字化转型，特别是医学成像与临床数据的整合，在个性化医疗中至关重要。观察性医疗结果伙伴关系（OMOP）通用数据模型（CDM）标准化了健康数据。然而,整合医学成像仍然是一个挑战。
目的：本研究提出了一种将医学成像数据与OMOPCDM相结合的方法，以改善多模态研究。
方法：我们的方法包括分析和选择医学标题标签中的数字成像和通信，数据格式的验证，并根据OMOP清洁发展机制框架进行调整。快速医疗保健互操作性资源ImagingStudy简介指导了我们在列命名和定义方面的一致性。成像通用数据模型(I-CDM)使用实体-属性-值模型构建，促进可扩展和高效的医学成像数据管理。对于2010年至2017年间诊断为肺癌的患者，我们引入了4个新的表格-IMAGING_STEST，IMAGING_SERIES,IMAGING_ANNOTATION,和FILEPATH-标准化各种影像学相关数据并链接到临床数据。
结果：该框架强调了I-CDM在增强我们对肺癌诊断和治疗策略的理解方面的有效性。I-CDM表的实施使全面的数据集能够结构化组织，包括282,098个图像研究，5,674,425图像系列，和48,536个图像注释记录，说明了该方法的广泛范围和深度。使用肺癌患者的实际数据进行的基于情景的分析强调了我们方法的可行性。应用44条特定规则的数据质量检查确认了构建的数据集的高度完整性，所有检查都成功通过，强调了我们研究结果的可靠性。
结论：这些发现表明I-CDM可以改善医学影像和临床数据的整合和分析。通过解决数据标准化和管理方面的挑战，我们的方法有助于加强诊断和治疗策略.未来的研究应该将I-CDM的应用扩展到不同的疾病人群，并探索其在医疗条件下的广泛用途。
目的：本研究提出了一种将医学成像数据与OMOPCDM相结合的方法，以改善多模态研究。
方法：我们的方法包括分析和选择医学标题标签中的数字成像和通信，数据格式的验证，并根据OMOP清洁发展机制框架进行调整。快速医疗保健互操作性资源ImagingStudy简介指导了我们在列命名和定义方面的一致性。成像通用数据模型(I-CDM)使用实体-属性-值模型构建，促进可扩展和高效的医学成像数据管理。对于2010年至2017年间诊断为肺癌的患者，我们引入了4个新的表格-IMAGING_STEST，IMAGING_SERIES,IMAGING_ANNOTATION,和FILEPATH-标准化各种影像学相关数据并链接到临床数据。
结果：该框架强调了I-CDM在增强我们对肺癌诊断和治疗策略的理解方面的有效性。I-CDM表的实施使全面的数据集能够结构化组织，包括282,098个图像研究，5,674,425图像系列，和48,536个图像注释记录，说明了该方法的广泛范围和深度。使用肺癌患者的实际数据进行的基于情景的分析强调了我们方法的可行性。应用44条特定规则的数据质量检查确认了构建的数据集的高度完整性，所有检查都成功通过，强调了我们研究结果的可靠性。
结论：这些发现表明I-CDM可以改善医学影像和临床数据的整合和分析。通过解决数据标准化和管理方面的挑战，我们的方法有助于加强诊断和治疗策略.未来的研究应该将I-CDM的应用扩展到不同的疾病人群，并探索其在医疗条件下的广泛用途。
