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Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data

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Purpose: Digital 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 Methods: R-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. Results: Information 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 RCDM were linked to efficiently extract specific types of images from specific patient cohorts. Conclusion: R-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.

Abstract

Purpose: Digital 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 Methods: R-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. Results: Information 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 RCDM were linked to efficiently extract specific types of images from specific patient cohorts. Conclusion: R-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.

참고문헌 (33)

  1. Soffer S, "Convolutional neural networks for radiologic images : a radiologist’s guide", Radiology, 290, pp.590~606 (2019)
  2. Mildenberger P, "Introduction to the DICOM standard", Eur Radiol, 12, pp.920~927 (2002)
  3. Willemink MJ, "Preparing medical imaging data for machine learning", Radiology, 295, pp.4~15 (2020)
  4. Basu A, "Call for data standardization : lessons learned and recommendations in an imaging study", JCO Clin Cancer Inform, 3, pp.1~11 (2019)
  5. Gueld MO, "Quality of DICOM header information for image categorization", Medical Imaging, 4685, pp.280~287 (2002)
  6. Gauriau R, "Using DICOM metadata for radiological image series categorization : a feasibility study on large clinical brain MRI datasets", J Digit Imaging, 33, pp.747~762 (2020)
  7. Blackledge J, "Stegacryption of DICOM metadata", Proceedings of the 25th IET Irish Signals & Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communications Technologies(ISSC 2014, 2014, 304~309[DOI]
  8. Hripcsak G, "Observational health data sciences and informatics(OHDSI) : opportunities for observational researchers", Stud Health Technol Inform, 216, pp.574~578 (2015)
  9. Vreeman DJ, "The LOINC RSNA radiology playbook-a unified terminology for radiology procedures", J Am Med Inform Assoc, 25, pp.885~893 (2018)
  10. Hripcsak G, "Facilitating phenotype transfer using a common data model", J Biomed Inform, 96, pp.103253~ (2019)
  11. Hong Y, "Analysis of RadLex coverage and term co-occurrence in radiology reporting templates", J Digit Imaging, 25, pp.56~62 (2012)
  12. Peng P, "Mapping of HIE CT terms to LOINC® : analysis of content-dependent coverage and coverage improvement through new term creation", J Am Med Inform Assoc, 26, pp.19~27 (2019)
  13. Matcho A, "Fidelity assessment of a clinical practice research datalink conversion to the OMOP common data model", Drug Saf, 37, pp.945~959 (2014)
  14. Reich C, "Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases", J Biomed Inform, 45, pp.689~696 (2012)
  15. Lima DM, "Transforming two decades of ePR data to OMOP CDM for clinical research", Stud Health Technol Inform, 264, pp.233~237 (2019)
  16. Müller H, "A review of content-based image retrieval systems in medical applications-clinical benefits and future directions", Int J Med Inform, 73, pp.1~23 (2004)
  17. Akgül CB, "Content-based image retrieval in radiology : current status and future directions", J Digit Imaging, 24, pp.208~222 (2011)
  18. Li Z, "Large-scale retrieval for medical image analytics : a comprehensive review", Med Image Anal, 43, pp.66~84 (2018)
  19. Pizarro R, "Using deep learning algorithms to automatically identify the brain MRI contrast : implications for managing large databases", Neuroinformatics, 17, pp.115~130 (2019)
  20. Beam AL, "Challenges to the reproducibility of machine learning models in health care", JAMA, 323, pp.305~306 (2020)
  21. Allen B Jr, "A road map for translational research on artificial intelligence in medical imaging : from the 2018 National Institutes of Health", RSNA, The Academy Workshop, pp.J Am Coll Radiol, 2019[DOI]
  22. Reps JM, "Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data", J Am Med Inform Assoc, 25, pp.969~975 (2018)
  23. Burn E, "Deep phenotyping of 34, 128 adult patients hospitalised with COVID-19 in an international network study", Nat Commun, 11, pp.5009~ (2020)
  24. "Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 vulnerability (C-19) index across an international network of observational healthcare datasets" (2020)[URL]
  25. "Seek COVER: development and validation of a personalized risk calculator for COVID-19 outcomes in an international network" (2020)[URL]
  26. Langer SG, "Collaborative and reproducible research : goals, challenges, and strategies", J Digit Imaging, 31, pp.275~282 (2018)
  27. Kohli MD, "Medical image data and datasets in the era of machine learning—whitepaper from the 2016 C-MIMI meeting dataset session", J Digit Imaging, 30, pp.392~399 (2017)
  28. Shin SJ, "Genomic common data model for seamless interoperation of biomedical data in clinical practice : retrospective study", J Med Internet Res, 21, pp.e13249~ (2019)
  29. Belenkaya R, "Extending the OMOP common data model and standardized vocabularies to support observational cancer research", JCO Clin Cancer Inform, 5, pp.12~20 (2021)
  30. Rubin DL, "Common data elements in radiology", Radiology, 283, pp.837~844 (2017)
  31. Sharma H, "Developing a portable natural language processing based phenotyping system", BMC Med Inform Decis Mak, 19, pp.78~ (2019)
  32. Park J, "A framework(SOCRATex)for hierarchical annotation of unstructured electronic health records and integration into a standardized medical database : development and usability study", JMIR Med Inform, 9, pp.e23983~ (2021)
  33. Mongkolwat P, "The national cancer informatics program(NCIP)annotation and image markup(AIM)foundation model", J Digit Imaging, 27, pp.692~701 (2014)

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