Evidence map›Paper›PMID 42006397›Full record

ReviewRadiology advances2026

Building trust and privacy in cross-border health data sharing for European cancer research.

Ricard Martínez Martínez, Ana de Marco, Ignacio Blanquer, Luis Martí-Bonmatí

Abstract readReview
In one paragraph

Review in Radiology advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Ricard Martínez MartínezInstituto Universitario de Investigación de Robótica y Tecnologías de la Información y Comunicación. Universitat de València , Valencia, 46980, Spain.ORCID https://orcid.org/0000-0003-3297-6385
Ana de MarcoBiomedical Imaging Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, 46026, Spain.ORCID https://orcid.org/0009-0004-6906-3779
Ignacio BlanquerInstituto de Instrumentación Para la Imagen, Universitat Politècnica de València, Valencia, 46022, Spain.ORCID https://orcid.org/0000-0003-1692-8922
Luis Martí-BonmatíBiomedical Imaging Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, 46026, Spain.ORCID https://orcid.org/0000-0002-8234-010X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data-driven research using artificial intelligence (AI) is transforming biomedical science, yet its application in medical imaging remains limited by fragmented datasets, heterogeneous legislation, and ethical uncertainties. The European Cancer Imaging Initiative (EUCAIM) addresses these barriers by establishing a federated, secure and interoperable European imaging infrastructure, fostering a trusted ecosystem for AI-enabled research. EUCAIM brings privacy, ethics, and security within a single, coherent operational framework. The project implements a risk-based, compliance-by-default approach that embeds Data Protection Impact Assessments throughout system design, translating legal requirements into verifiable technical safeguards. Its "de facto" anonymization model, aligned with the General Data Protection Regulation and Court of Justice jurisprudence, combines multi-stage anonymization pipelines, cryptographic hashing, and automated re-identification-risk analyses to deliver a federated Secure Processing Environment (SPE) for researchers. This federated infrastructure is consistent with the European Health Data Space Regulation (EHDSR) and national security frameworks, and ensures data sovereignty, interoperability, and accountability. A comprehensive governance and contractual framework, including Data Sharing and Transfer Agreements, clearly delineates roles and responsibilities, while the Data Access Committee provides robust ethical oversight. EUCAIM thus offers a lawful, secure, and sustainable model of a federated secure environment for the reuse of imaging data, advancing a genuinely data-driven research ecosystem.

Indexed as

anonymizationcancer imagingdata governanceEuropean Health Data Spacefederated learningGDPR

Identifiers

PMID42006397
PMCPMC13089403

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.