Evidence map›Paper›PMID 39992532›Full record

ArticleInsights into imaging2025

Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure.

Luis Martí-Bonmatí, Ignacio Blanquer, Manolis Tsiknakis, Gianna Tsakou, Ricard Martinez, Salvador Capella-Gutierrez, Sara Zullino, Janos Meszaros, Esther E Bron, Jose Luis Gelpi and 9 more

Abstract read
In one paragraph

Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

19 authors.

Luis Martí-BonmatíBiomedical Imaging Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, Spain. luis_marti@iislafe.es.ORCID http://orcid.org/0000-0002-8234-010X
Ignacio BlanquerUniversitat Politècnica de València, Valencia, Spain.
Manolis TsiknakisFoundation for Research and Technology, Hellas, Crete, Greece.
Gianna TsakouMaggioli SPA, Research and Development Lab, Athens, Greece.
Ricard MartinezUniversitat de València, Valencia, Spain.
Salvador Capella-GutierrezBarcelona Supercomputing Center, Barcelona, Spain.
Sara ZullinoEATRIS ERIC, Amsterdam, The Netherlands.
Janos MeszarosCenter for IT and IP Law, KU Leuven, Leuven, Belgium.
Esther E BronBiomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Jose Luis GelpiUniversitat De Barcelona, Barcelona, Spain.
Katrine RiklundDepartment of Diagnostics and Interventions, Umea Universitet, Umeå, Sweden.
Linda ChaabaneEURO-BIOIMAGING ERIC, Med-Hub, Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Turin, Italy.
Heinz-Peter SchlemmerDivision of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Mario AznarMatical Innovation, Madrid, Spain.
Patricia Serrano CandelasBiomedical Imaging Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, Spain.
Peter GordebekeEIBIR Gemeinnutzige Gmbh Zur Forderung Der Erforschung Der Biomedizinischen Bildgebung, Vienna, Austria.
Monika HierathEIBIR Gemeinnutzige Gmbh Zur Forderung Der Erforschung Der Biomedizinischen Bildgebung, Vienna, Austria.
EUCAIM Consortium
European Society of Radiology

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is a powerful technology with the potential to disrupt cancer detection, diagnosis and treatment. However, the development of new AI algorithms requires access to large and complex real-world datasets. Although such datasets are constantly being generated, access to them is limited by data fragmentation across numerous repositories and sites, heterogeneity, lack of annotations, and potential privacy issues. The European Cancer Imaging Initiative is a flagship of Europe's Beating Cancer Plan, aiming to unlock the power of AI for cancer patients, clinicians, and researchers by establishing a federated European infrastructure for cancer images through the EU-funded EUropean Federation for CAncer IMages (EUCAIM) project. This infrastructure, called Cancer Image Europe, builds on the AI for Health Imaging network (AI4HI), established European Research Infrastructures (Euro-BioImaging, BBMRI-ERIC, EATRIS, ECRIN, and ELIXIR), and numerous related partners providing access to research tools, images, and related clinical, pathology and molecular data. The infrastructure targets clinicians, researchers, and innovators by providing the means to develop and validate data-intensive AI-based and other IT-enabled clinical decision-making systems supporting precision medicine. Common data models, including a linking hyperontology, quality standards, compliance with the FAIR (Findability, Accessibility, Interoperability and Reusability) principles, data annotation, curation and anonymization services are provided to ensure data quality and interoperability, consistency and privacy. In summer 2024, the EUCAIM project released the first prototype of an EU-wide infrastructure, with a comprehensive dashboard integrating applications for dataset discovery, federated search, data access request, metadata harvesting, annotation, secure processing environments and federated processing. CRITICAL RELEVANCE STATEMENT: EUCAIM's federated infrastructure for cancer image data advances medical research and related AI development in Europe. It addresses the current fragmentation and heterogeneity of data repositories is legally compliant, and facilitates collaboration among clinicians, researchers, and innovators. KEY POINTS: AI solutions to advance cancer care rely on large, high-quality real-world datasets. EUCAIM's federated infrastructure for cancer image data empowers cancer research in Europe. It provides access to research tools, images, and related clinical, pathology and molecular data.

Indexed as

Artificial intelligenceCancer researchEuropean Health Data SpaceImagingInfrastructure

Identifiers

PMID39992532
PMCPMC11850660

What OpenQuestion holds

Textmetadata
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.