Iancu A, Bauer J, May M, Prokosch HU, Dörfler A, Uder M, Kapsner L (2025)
Publication Type: Journal article
Publication year: 2025
DOI: 10.1055/a-2521-4250
Background The current gap between the availability of routine imaging data and its provisioning for medical research hinders the utilization of radiological information for secondary purposes. To address this, the German Medical Informatics Initiative (MII) has established frameworks for harmonizing and integrating clinical data across institutions, including the integration of imaging data into research repositories, which can be expanded to routine imaging data. Objectives This project aims to address this gap by developing a large-scale data processing pipeline to extract, convert, and pseudonymize DICOM (Digital Imaging and Communications in Medicine) metadata into ImagingStudy Fast Healthcare Interoperability Resources (FHIR) and integrate them into research repositories for secondary use. Methods The data processing pipeline was developed, implemented, and tested at the Data Integration Center of the University Hospital Erlangen. It leverages existing open-source solutions and integrates seamlessly into the hospital's research IT infrastructure. The pipeline automates the extraction, conversion, and pseudonymization processes, ensuring compliance with both local and MII data protection standards. A large-scale evaluation was conducted using the imaging studies acquired by two departments at University Hospital Erlangen within 1 year. Attributes such as modality, examined body region, laterality, and the number of series and instances were analyzed to assess the quality and availability of the metadata. Results Once established, the pipeline processed a substantial dataset comprising over 150,000 DICOM studies within an operational period of 26 days. Data analysis revealed significant heterogeneity and incompleteness in certain attributes, particularly the DICOM tag Body Part Examined. Despite these challenges, the pipeline successfully generated valid and standardized FHIR, providing a robust basis for future research. Conclusion We demonstrated the setup and test of a large-scale end-to-end data processing pipeline that transforms DICOM imaging metadata directly from clinical routine into the Health Level 7-FHIR format, pseudonymizes the resources, and stores them in an FHIR server. We showcased that the derived FHIRs offer numerous research opportunities, for example, feasibility assessments within Bavarian and Germany-wide research infrastructures. Insights from this study highlight the need to extend the ImagingStudy FHIR with additional attributes and refine their use within the German MII.
APA:
Iancu, A., Bauer, J., May, M., Prokosch, H.-U., Dörfler, A., Uder, M., & Kapsner, L. (2025). Large-Scale Integration of DICOM Metadata into HL7-FHIR for Medical Research. Methods of Information in Medicine. https://doi.org/10.1055/a-2521-4250
MLA:
Iancu, Alexa, et al. "Large-Scale Integration of DICOM Metadata into HL7-FHIR for Medical Research." Methods of Information in Medicine (2025).
BibTeX: Download