Evidence map›Paper›PMID 35001159›Full record

ReviewEuropean radiology2022

Bridging gaps between images and data: a systematic update on imaging biobanks.

Michela Gabelloni, Lorenzo Faggioni, Rita Borgheresi, Giuliana Restante, Jorge Shortrede, Lorenzo Tumminello, Camilla Scapicchio, Francesca Coppola, Dania Cioni, Ignacio Gómez-Rico and 2 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
–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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Review
  16. Review
  17. Article
  18. Review
  19. Article
  20. 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

12 authors.

Michela GabelloniAcademic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy.
Lorenzo FaggioniAcademic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy. lfaggioni@sirm.org.ORCID http://orcid.org/0000-0001-5262-4489
Rita BorgheresiAcademic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy.
Giuliana RestanteAcademic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy.
Jorge ShortredeAcademic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy.
Lorenzo TumminelloAcademic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy.
Camilla ScapicchioAcademic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy.
Francesca CoppolaDepartment of Radiology, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, 40138, Bologna, Italy.
Dania CioniAcademic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy.
Ignacio Gómez-RicoBiomedical Imaging Research Group (GIBI230-PREBI) at La Fe Health Research Institute, 46026, Valencia, Spain.
Luis Martí-BonmatíBiomedical Imaging Research Group (GIBI230-PREBI) at La Fe Health Research Institute, 46026, Valencia, Spain.
Emanuele NeriAcademic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy.

Funding

Horizon 2020 CHAIMELEON 952172Horizon 2020 EuCanImage 952103Horizon 2020 PRIMAGE 826494Horizon 2020 ProCancer-I 952159
6 · The paper itself

Abstract

BACKGROUND AND

objectiveThe systematic collection of medical images combined with imaging biomarkers and patient non-imaging data is the core concept of imaging biobanks, a key element for fuelling the development of modern precision medicine. Our purpose is to review the existing image repositories fulfilling the criteria for imaging biobanks.

methodsPubmed, Scopus and Web of Science were searched for articles published in English from January 2010 to July 2021 using a combination of the terms: "imaging" AND "biobanks" and "imaging" AND "repository". Moreover, the Community Research and Development Information Service (CORDIS) database ( https://cordis.europa.eu/projects ) was searched using the terms: "imaging" AND "biobanks", also including collections, projects, project deliverables, project publications and programmes.

resultsOf 9272 articles retrieved, only 54 related to biobanks containing imaging data were finally selected, of which 33 were disease-oriented (61.1%) and 21 population-based (38.9%). Most imaging biobanks were European (26/54, 48.1%), followed by American biobanks (20/54, 37.0%). Among disease-oriented biobanks, the majority were focused on neurodegenerative (9/33, 27.3%) and oncological diseases (9/33, 27.3%). The number of patients enrolled ranged from 240 to 3,370,929, and the imaging modality most frequently involved was MRI (40/54, 74.1%), followed by CT (20/54, 37.0%), PET (13/54, 24.1%), and ultrasound (12/54, 22.2%). Most biobanks (38/54, 70.4%) were accessible under request.

conclusionsImaging biobanks can serve as a platform for collecting, sharing and analysing medical images integrated with imaging biomarkers, biological and clinical data. A relatively small proportion of current biobanks also contain images and can thus be classified as imaging biobanks. KEY POINTS: • Imaging biobanks are a powerful tool for large-scale collection and processing of medical images integrated with imaging biomarkers and patient non-imaging data. • Most imaging biobanks retrieved were European, disease-oriented and accessible under user request. • While many biobanks have been developed so far, only a relatively small proportion of them are imaging biobanks.

Indexed as

Biological Specimen BanksPrecision MedicineBiomarkersDatabases, FactualDiagnostic ImagingHumansBiomarkersArtificial intelligenceBiomarkersDiagnostic imaging

Identifiers

What OpenQuestion holds

Textmetadata
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.