Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure

作者：Luis Martí‐Bonmatí, Ignácio Blanquer, Manolis Tsiknakis, Gianna Tsakou, Ricard Martínez Martínez, Salvador Capella-Gutiérrez, Sara Zullino, J. Gary Meszaros, Esther E. Bron, Josep Lluis Gelpí, Katrine Riklund, Linda Chaabane, Heinz‐Peter Schlemmer, Mario Aznar, Patricia Serrano Candelas, Peter Gordebeke, Monika Hierath, Hanna Leisz, N. d’Ascenzo, Miguel Castelo-Branco, Ioanna Chouvarda, Laure Fournier, Sergio Figueiras Gómez, Arcadi Navarro, Nikolaos Papanikolaou, Laurent Barbieri, Jean-Paul Bérégi, Georg Langs, David Rodríguez González, Evis Sala, Yiannis Roussakis, Giovanni Di Leo, Antonio López‐Rueda, Salvador Pedraza, Javier Blázquez, Carlos Luís Parra-Calderón, Silvia Marsoni, Daniel Sáez-Domingo, Marco Aiello, Fredrik Strand, Marie‐Christine Jaulent, Joel Hedlund, Laure Saint‐Aubert, Carlo Catalano, Geerard L. Beets, Harald S. Heese, Ángel Alberich‐Bayarri, Bram van Ginneken, Marc Van den Bulcke, Daniel Rückert, Emanuele Neri, Philippe Lambin, Gernot Marx, Bengt Persson, Wim Vos, Cátia Sousa Pinto, Patrick Fuhrmann, Maciej Bobowicz, Carles Hernandéz-Ferrer, Olivier Humbert, José Miguel Rosell Tejada, Manuela França, Petr Holub, Zdenka Dudová, Isabelle Huys, Matteo Pallocca, Johannes Haybaeck, Patrycja Gazińska, Annabel Seebohm, Tobias Penzkofer, N Sandberg, Oscar Gil García, Fernando Martín-Sánchez, Serena Scollen, European Society of Radiology · 发表于：Insights into Imaging · 年份：2025 · DOI：10.1186/s13244-025-01913-x · 被引用次数：25 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Artificial Intelligence in Healthcare and Education、AI in cancer detection

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...