ArticleYonsei medical journal2022
Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data.
Article in Yonsei medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Extending the Observational Medical Outcomes Partnership Common Data Model to Support Observational Peripheral Vascular Disease Research.The Journal of surgical research · 2026Observational
- Data harmonization processes of cancer data into the observational medical outcomes partnership common data model.Scientific reports · 2026Article
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Automated Integration of AI Results into Radiology Reports Using Common Data Elements.Journal of imaging informatics in medicine · 2025Article
- The march to harmonized imaging standards for retinal imaging.Progress in retinal and eye research · 2025Review
- Lessons Learned From European Health Data Projects With Cancer Use Cases: Implementation of Health Standards and Internet of Things Semantic Interoperability.Journal of medical Internet research · 2025Article
- Analysis of Retinal Thickness in Patients With Chronic Diseases Using Standardized Optical Coherence Tomography Data: Database Study Based on the Radiology Common Data Model.JMIR medical informatics · 2025Article
- Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.Investigative radiology · 2025Review
- Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.JMIR medical informatics · 2024Article
- Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension.Journal of imaging informatics in medicine · 2024Article
- Artificial Intelligence Reporting Guidelines' Adherence in Nephrology for Improved Research and Clinical Outcomes.Biomedicines · 2024Review
- Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach.Scientific reports · 2024Article
- MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes.JCO clinical cancer informatics · 2023Article
- Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review.JMIR medical informatics · 2023Article
- Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review.JMIR medical informatics · 2023Review
- Methods Used in the Development of Common Data Models for Health Data: Scoping Review.JMIR medical informatics · 2023Article
- Ten Topics to Get Started in Medical Informatics Research.Journal of medical Internet research · 2023Article
- Common measures in mental health: a joint initiative by funders and journals.The lancet. Psychiatry · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeDigital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM). MATERIALS AND
methodsR-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database.
resultsInformation from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and R-CDM were linked to efficiently extract specific types of images from specific patient cohorts.
conclusionR-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.
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