Integrating Clinical Data and Medical Imaging in Lung Cancer: A Feasibility Study Using the OMOP Common Data Model Extension (Preprint)

BACKGROUND Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership Common Data Model (OMOP 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 (DICOM) header tags, validation of data formats, and alignment according to the OMOP CDM framework. The FHIR ImagingStudy profile guided our consistency in column naming and definitions. Medical Imaging CDM (MI-CDM), constructed using the entity-attribute-value (EAV) model, facilitates scalable and efficient MI data management. For lung cancer patients diagnosed between 2010 and 2017, we introduced four 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 MI-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the MI-CDM tables enabled the structured organization of a comprehensive dataset, including 275,446 IMAGING_STUDY, 5,346,571 IMAGING_SERIES, and 34,449 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 dataset, with all checks successfully passed, underscoring the reliability of our findings. CONCLUSIONS These findings indicate that MI-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 MI-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.