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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Background and objective

The 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.

Methods

Pubmed, 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.

Results

Of 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.

Conclusions

Imaging 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.

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Abbreviations

AI:

Artificial intelligence

BBMRI-ERIC:

Biobanking and BioMolecular resources Research Infrastructure — European Research Infrastructure Consortium

CARDIATEAM:

CARdiomyopathy in type 2 DIAbetes mellitus

COPD:

Chronic obstructive pulmonary disease

CORDIS:

Community Research and Development Information Service

DIPG:

Diffuse intrinsic pontine glioma

DMG:

Diffuse midline glioma

DXA:

Dual-energy X-ray absorptiometry

ESR:

European Society of Radiology

FAIR:

Findability, Accessibility, Interoperability, and Reusability

GDPR:

General Data Protection Regulation

HPCI:

High-performance computing infrastructure

MESA:

Multi-Ethnic Study of Atherosclerosis

MIABIS:

Minimum Information About BIobank data Sharing

Mobit:

Molecular Biomarkers for Individualized Therapy

OMOP CDM:

Observational Medical Outcomes Partnership Common Data Model

PRIMAGE:

PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

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Acknowledgements

The authors wish to thank Dr. Laura Landi (clinical trials office) for her kind support in manuscript preparation.

Funding

This study has been partially funded by the HORIZON 2020 projects CHAIMELEON, Grant agreement #952172, PRIMAGE, Grant agreement #826494, EuCanImage, Grant agreement #952103 and Procancer-I, Grant agreement #952159.

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Authors and Affiliations

  1. Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy

    Michela Gabelloni, Lorenzo Faggioni, Rita Borgheresi, Giuliana Restante, Jorge Shortrede, Lorenzo Tumminello, Camilla Scapicchio, Dania Cioni & Emanuele Neri

  2. Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, 40138, Bologna, Italy

    Francesca Coppola

  3. Italian Society of Medical and Interventional Radiology, SIRM Foundation, via della Signora, 2, 20122, Milan, Italy

    Francesca Coppola & Emanuele Neri

  4. Biomedical Imaging Research Group (GIBI230-PREBI) at La Fe Health Research Institute, 46026, Valencia, Spain

    Ignacio Gómez-Rico & Luis Martí-Bonmatí

  5. Medical Imaging Department, Hospital Universitario y Politécnico La Fe, 46026, Valencia, Spain

    Luis Martí-Bonmatí

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  1. Michela Gabelloni
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Michela Gabelloni and Lorenzo Faggioni are co-first authors.

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Gabelloni, M., Faggioni, L., Borgheresi, R. et al. Bridging gaps between images and data: a systematic update on imaging biobanks. Eur Radiol 32, 3173–3186 (2022). https://doi.org/10.1007/s00330-021-08431-6

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