The European Cancer Imaging Initiative is building a federated infrastructure for secure cross-border reuse of cancer imaging data across Europe, according to a recent analysis published in Radiology Advances. The framework addresses persistent barriers linked to fragmented datasets, heterogeneous legislation and ethical uncertainty across the European Union. By integrating legal, technical and ethical mechanisms into a single operational model, the initiative enables artificial intelligence-driven research while maintaining data protection requirements. The approach combines anonymisation strategies, governance structures and secure processing environments to support large-scale collaboration. It aligns with evolving European regulatory frameworks and seeks to provide a practical pathway for data-driven research that remains compliant, interoperable and accountable across multiple jurisdictions.
Challenges in Cross-Border Data Access
Cross-border use of medical imaging data within the European Union faces significant constraints driven by legal complexity and regulatory fragmentation. The General Data Protection Regulation introduces strict requirements for processing health data while allowing Member States to define national rules, resulting in inconsistent practices across jurisdictions. Legal processes for accessing datasets are often time-consuming and lack clarity, creating uncertainty for research institutions.
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Anonymisation remains a central requirement for large-scale data use, yet it presents technical and legal challenges. Data Protection Authorities frequently emphasise the difficulty of achieving full anonymisation, which limits the development of practical solutions for data reuse. As a result, pseudonymisation has become widely used, although it typically requires explicit patient consent in most jurisdictions. This condition restricts the scalability of research datasets and limits their applicability for artificial intelligence methods.
These constraints contribute to the formation of national data silos, reducing opportunities for pan-European collaboration. Institutional policies, ethical oversight and internal governance structures further restrict data sharing. Research datasets often prioritise statistical representativeness rather than scale, limiting their suitability for advanced analytical methods. Regulatory asymmetries across Member States continue to hinder the creation of integrated research infrastructures capable of supporting large, diverse imaging cohorts.
Federated Infrastructure and Anonymisation Strategy
EUCAIM introduces a federated architecture designed to overcome fragmentation by enabling data access without requiring data transfer across borders. Imaging datasets remain under the control of local institutions while being made accessible through secure, distributed processing environments. This model supports interoperability while preserving data sovereignty at the source.
A central component of the framework is a risk-based, compliance-by-design approach that embeds Data Protection Impact Assessments into system development. These assessments guide decisions on dataset integration, access controls and processing workflows. Each component, including algorithms and multicentre studies, undergoes evaluation to ensure that privacy safeguards are operationally enforced.
The anonymisation strategy follows a dual approach combining local data processing and environment-level safeguards. Direct identifiers are removed at the source, while additional measures such as metadata stripping, date generalisation and domain-specific transformations reduce re-identification risk. Cryptographic hashing enables secure linkage of datasets without exposing identifiable information. Access is restricted to controlled interfaces within secure environments, preventing data export and limiting user actions.
This approach establishes a condition in which re-identification requires disproportionate effort, aligning with regulatory interpretations of anonymisation. Both imaging and clinical data undergo staged processing to minimise identifiable elements. Automated risk analyses assess datasets before integration, ensuring that only compliant data are included. The framework enables researchers to perform analyses within secure environments while maintaining strict privacy controls.
Governance, Legal Framework and Secure Processing
The infrastructure operates within a comprehensive governance model that integrates legal accountability, ethical oversight and technical control. A federated Secure Processing Environment ensures that all data processing occurs within monitored and controlled conditions. Access is limited to authorised users with defined permissions, supported by authentication mechanisms, activity logging and audit trails.
Participation in the federated system follows a tiered structure based on levels of compliance and technical maturity. Institutions can progressively align with interoperability, security and governance requirements, enabling gradual integration into the network. This model supports scalability while maintaining consistent standards across participating nodes.
Legal arrangements define responsibilities across data controllers, processors and participating institutions. Data Sharing Agreements and Data Transfer Agreements regulate the movement and use of data within the system. These instruments extend beyond data protection to include intellectual property safeguards, security obligations and operational conditions. Individual users must accept platform terms and commit to confidentiality and non-reidentification obligations, establishing a clear chain of responsibility.
Oversight is provided by a Data Access Committee that evaluates data access requests and verifies compliance with ethical, legal and technical requirements. The governance structure incorporates specialised roles in data protection, information security and data management, reflecting the complexity of the ecosystem. Training and operational alignment ensure that regulatory requirements are embedded into routine workflows across institutions.
The infrastructure also integrates contractual and organisational mechanisms that support transparency and accountability. Engagement with stakeholders, including healthcare professionals and public authorities, contributes to trust and long-term sustainability. The model provides a structured approach to managing data access, processing and oversight within a complex regulatory environment.
The EUCAIM framework establishes a federated model that combines anonymisation, governance and secure processing to enable cross-border reuse of cancer imaging data. By embedding regulatory requirements into technical and operational workflows, the system supports large-scale research while maintaining compliance and data protection. The integration of legal, ethical and technical elements creates a structured environment for collaboration across multiple jurisdictions. This approach demonstrates how interoperability and privacy safeguards can coexist within a unified infrastructure, providing a practical foundation for future European health data ecosystems.
Source: Radiology Advances
Image Credit: iStock
References:
Martínez R, de Marco A, Blanquer I & Martí-Bonmatí L (2026) Building Trust and Privacy in Cross-Border Health Data Sharing for European Cancer Research. Radiology Advances: umag018.
