Evidence map›Paper›PMID 40750795›Full record

ArticleScientific data2025

Multimodal data curation via interoperability: use cases with the Medical Imaging and Data Resource Center.

Weijie Chen, Heather M Whitney, Seyed Kahaki, Christopher Meyer, Hui Li, Rui Carlos Sá, Diane Lauderdale, Sandy Napel, Kenneth Gersing, Robert L Grossman and 1 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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.

Weijie Chen *Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, CDRH, US FDA, Silver Spring, MD, USA. weijie.chen@fda.hhs.gov.
Heather M Whitney *Department of Radiology, University of Chicago, Chicago, IL, USA. hwhitney@uchicago.edu.ORCID 0000-0002-7258-1102
Seyed KahakiDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, CDRH, US FDA, Silver Spring, MD, USA.
Christopher MeyerCenter for Translational Data Science, University of Chicago, Chicago, IL, USA.
Hui LiDepartment of Radiology, University of Chicago, Chicago, IL, USA.
Rui Carlos SáNational Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA.
Diane LauderdaleDepartment of Public Health Sciences, University of Chicago, Chicago, IL, USA.
Sandy NapelDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-6876-5507
Kenneth GersingOffice of the Director, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA.
Robert L GrossmanCenter for Translational Data Science, University of Chicago, Chicago, IL, USA.ORCID 0000-0003-3741-5739
Maryellen L GigerDepartment of Radiology, University of Chicago, Chicago, IL, USA.ORCID 0000-0001-5482-9728

Funding

MEDICAL IMAGING AND DATA RESOURCE CENTER (MIDRC) YEAR 375N92020D00021 · NIBIB · UNIVERSITY OF CHICAGO · PI GIGER, MARYELLEN · 2021 to 2025
$16.6M
NHLBI NIH HHS 75N92020D00021
6 · The paper itself

Abstract

Interoperability (the ability of data or tools from non-cooperating resources to integrate or work together with minimal effort) is particularly important for curation of multimodal datasets from multiple data sources. The Medical Imaging and Data Resource Center (MIDRC), a multi-institutional collaborative initiative to collect, curate, and share medical imaging datasets, has made interoperability with other data commons one of its top priorities. The purpose of this study was to demonstrate the interoperability between MIDRC and two other data repositories, BioData Catalyst (BDC) and National Clinical Cohort Collaborative (N3C). Using interoperability capabilities of the data repositories, we built two cohorts for example use cases, with each containing clinical and imaging data on matched patients. The representativeness of the cohorts is characterized by comparing with CDC population statistics using the Jensen-Shannon distance. The process and methods of interoperability demonstrated in this work can be utilized by MIDRC, BDC, and N3C users to create multimodal datasets for development of artificial intelligence/machine learning models.

Indexed as

Data CurationDiagnostic ImagingHealth Information InteroperabilityHumans

Identifiers

PMID40750795
PMCPMC12316880

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.