Evidence map›Paper›PMID 33681459›Full record

ArticleTomography (Ann Arbor, Mich.)2021

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 and 1 more

Abstract read
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Data Format Standardization and DICOM Integration for HyperpolarizedJournal of imaging informatics in medicine · 2024
    Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Joseph D KalenSmall Animal Imaging Program, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.ORCID 0000-0002-7163-4604
David A CluniePixelMed Publishing, Bangor, PA 18013, USA.ORCID 0000-0002-2406-1145
Yanling LiuImage 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 TatumCancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institute of Health, Rockville, MD 20892, USA.ORCID 0000-0002-3217-2478
Paula M JacobsCancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institute of Health, Rockville, MD 20892, USA.ORCID 0000-0002-9423-6473
Justin KirbyCancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.ORCID 0000-0003-3487-8922
John B FreymannCancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
Ulrike WagnerBiomedical Informatics and Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.ORCID 0000-0002-3230-5058
Kirk E SmithDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0002-8735-7576
Christian SulowayImage and Visualization Group, Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.ORCID 0000-0002-6710-503X
James H DoroshowDivision of Cancer Treatment and Diagnosis, Center for Cancer Research, National Cancer Institute, National Institute of Health, Rockville, MD 20892, USA.ORCID 0000-0002-4463-1790

Funding

Phase 01 Clinical TrialsZIABC011078 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI DOROSHOW, JAMES · 2009 to 2025
$9.0M
6 · The paper itself

Abstract

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.

Indexed as

Radiology Information SystemsAnimalsCohort StudiesMagnetic Resonance ImagingMicePositron Emission Tomography Computed Tomographyanimal modelco-clinicalDICOMin vivo imagingpatient-derived xenograft (PDX)pre-clinical

Identifiers

PMID33681459
PMCPMC7934703

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.