Design and Implementation of the Pre-Clinical DICOM Standard in Multi-Cohort Murine Studies
Joseph D. Kalen,
David A. Clunie,
Yanling Liu,
James L. Tatum,
Paula M. Jacobs,
Justin Kirby,
John B. Freymann,
Ulrike Wagner,
Kirk E. Smith,
Christian Suloway,
James H. Doroshow
Affiliations
Joseph D. Kalen
Small Animal Imaging Program, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
David A. Clunie
PixelMed Publishing, Bangor, PA 18013, USA
Yanling Liu
Image and Visualization Group, Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
James L. Tatum
Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institute of Health, Rockville, MD 20892, USA
Paula M. Jacobs
Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institute of Health, Rockville, MD 20892, USA
Justin Kirby
Cancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
John B. Freymann
Cancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
Ulrike Wagner
Biomedical Informatics and Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
Kirk E. Smith
Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Christian Suloway
Image and Visualization Group, Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
James H. Doroshow
Division of Cancer Treatment and Diagnosis, Center for Cancer Research, National Cancer Institute, National Institute of Health, Rockville, MD 20892, USA
The small animal imaging Digital Imaging and Communications in Medicine (DICOM) acquisition context structured report (SR) was developed to incorporate pre-clinical data in an established DICOM format for rapid queries and comparison of clinical and non-clinical datasets. Established terminologies (i.e., anesthesia, mouse model nomenclature, veterinary definitions, NCI Metathesaurus) were utilized to assist in defining terms implemented in pre-clinical imaging and new codes were added to integrate the specific small animal procedures and handling processes, such as housing, biosafety level, and pre-imaging rodent preparation. In addition to the standard DICOM fields, the small animal SR includes fields specific to small animal imaging such as tumor graft (i.e., melanoma), tissue of origin, mouse strain, and exogenous material, including the date and site of injection. Additionally, the mapping and harmonization developed by the Mouse-Human Anatomy Project were implemented to assist co-clinical research by providing cross-reference human-to-mouse anatomies. Furthermore, since small animal imaging performs multi-mouse imaging for high throughput, and queries for co-clinical research requires a one-to-one relation, an imaging splitting routine was developed, new Unique Identifiers (UID’s) were created, and the original patient name and ID were saved for reference to the original dataset. We report the implementation of the small animal SR using MRI datasets (as an example) of patient-derived xenograft mouse models and uploaded to The Cancer Imaging Archive (TCIA) for public dissemination, and also implemented this on PET/CT datasets. The small animal SR enhancement provides researchers the ability to query any DICOM modality pre-clinical and clinical datasets using standard vocabularies and enhances co-clinical studies.
