Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure

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.

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Publication Details

Journal
Insights into Imaging
Published
2025-02-24
DOI
https://doi.org/10.1186/s13244-025-01913-x
Citations
18
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
24.48

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article

Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure

Tobias Penzkofer, Petr Holub, Heinz‐Peter Schlemmer, Óscar García et al.
18 citations
Insights into Imaging
Radiomics and Machine Learning in Medical Imaging
24.48
article

Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure

Tobias Penzkofer, Petr Holub, Heinz‐Peter Schlemmer, Óscar García, Gianna Tsakou, Wim Vos, Mario Aznar, Sara Zullino, Serena Scollen, Maciej Bobowicz, Jean-Paul Bérégi, Zdenka Dudová, Ricard Martínez Martínez, Antonio López‐Rueda, Monika Hierath, Ángel Alberich‐Bayarri, Giovanni Di Leo, Salvador Pedraza, N. d’Ascenzo, Lucia Barbieri, Esther E. Bron, Evis Sala, Harald S. Heese, Carlos Luís Parra-Calderón, J. Tejada, Nick Papanikolaou, Patrycja Gazińska, Marie‐Christine Jaulent, Sergio Figueiras Gómez, Carmine Pinto, Manuela França, Laure Saint‐Aubert, Bram van Ginneken, Joel Hedlund, Carles Hernandéz-Ferrer, Bengt Persson, Emanuele Neri, Fredrik Strand, Arcadi Navarro, Philippe Lambin, Salvador Capella-Gutiérrez, Georg Langs, Fernando Martín-Sánchez, Peter Gordebeke, J. Gary Meszaros, Gernot Marx, Katrine Riklund, Matteo Pallocca, Ignácio Blanquer, Luis Martí‐Bonmatí, Carlo Catalano, Marco Aiello, Ioanna Chouvarda, Silvia Marsoni, Geerard L. Beets, Johannes Haybaeck, Yiannis Roussakis, Laure Fournier, Isabelle Huys, Manolis Tsiknakis, Josep Lluís Gelpí, Linda Chaabane, D. Rodríguez, Miguel Castelo-Branco, Olivier Humbert, Javier Blázquez, Marc Van den Bulcke, N Sandberg, Daniel Rückert, Patricia Serrano Candelas, Hanna Leisz, Daniel Sáez-Domingo, Patrick Fuhrmann, Annabel Seebohm
article en
18 citations

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.

Insights into ImagingVol. 16(1)
Linköping University (SE), Università Cattolica del Sacro Cuore (IT), University of Pisa (IT), Champalimaud Foundation (PT), Uppsala University (SE), Centre National de la Recherche Scientifique (FR), Institut national de recherche en sciences et technologies du numérique (FR), Karolinska University Hospital (SE), Consejo Superior de Investigaciones Científicas (ES), Innsbruck Medical University (AT), Radboud University Nijmegen (NL), Andalusian Health Service (ES), Universitat de València (ES), German Cancer Research Center (DE), Universität Innsbruck (AT), Aristotle University of Thessaloniki (GR), Masaryk University (CZ), Heidelberg University (DE), Barcelona Supercomputing Center (ES), EGI (NL), Instituto de Salud Carlos III (ES), Erasmus MC (NL), Agostino Gemelli University Polyclinic (IT), Radboud University Medical Center (NL), The Netherlands Cancer Institute (NL), Digital Europe (BE), Istituto Neurologico Mediterraneo (IT), Maastricht University (NL), Escuela Nacional de Sanidad (ES), Biobanking and Biomolecular Resources Research Infrastructure Consortium (AT), Assistance Publique – Hôpitaux de Paris (FR), Instituto Químico Biológico (ES), IRCCS Policlinico San Donato (IT), Bioforschung Austria (AT), Oncode Institute (NL), Hospital Clínic de Barcelona (ES), Universitätsklinikum Aachen (DE), Madrid Health Service (ES), Hospital de Santo António (PT), Flemish Radio and Television Broadcasting Organisation (BE), European Institute for Biomedical Imaging Research (AT), Centro Tecnológico de Investigación, Desarrollo e Innovación en tecnologías de la Información y las Comunicaciones (TIC) (ES), Institute of Biostructure and Bioimaging (IT), Istituti Fisioterapici Ospitalieri (IT), Centre for Genomic Regulation (ES), Västerbotten County (SE), Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (FR), Sciensano (Belgium) (BE), Łukasiewicz Research Network – PORT Polish Center for Technology Development (PL), Genomics (United Kingdom) (GB), Instituto de Física de Cantabria (ES), Hospital Universitario Virgen del Rocío (ES), Czech Academy of Sciences, Institute of Computer Science (CZ), National Research Council (IT), Ministry of Health (PT), Fundació Clínic per a la Recerca Biomèdica (ES), Philips (Germany) (DE), S2 Grupo (Spain) (ES), Instituto de Investigación Sanitaria La Fe (ES), Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, Universitat Politècnica de València (ES), Technical University of Munich (DE), Universitat de Barcelona (ES), Gdańsk Medical University (PL), Medical University of Vienna (AT), University of Coimbra (PT), Charité - Universitätsmedizin Berlin (DE), Sapienza University of Rome (IT), Foundation for Research and Technology Hellas (GR), Umeå University (SE), Universitat Politècnica de Catalunya (ES), Software (Spain) (ES), KU Leuven (BE)
European Commission
Industry, innovation and infrastructure
Openalex Percentile: Top 0%
Radiomics and Machine Learning in Medical Imaging
24.48
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