ArticleGigaScience2025
New implementation of data standards for AI in oncology: Experience from the EuCanImage project.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Real-world evidence on data quality in precision oncology platforms: insights from the Molecular Twin Research Umbrella protocol.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
backgroundAn unprecedented amount of personal health data, with the potential to revolutionize precision medicine, is generated at health care institutions worldwide. The exploitation of such data using artificial intelligence (AI) relies on the ability to combine heterogeneous, multicentric, multimodal, and multiparametric data, as well as thoughtful representation of knowledge and data availability. Despite these possibilities, significant methodological challenges and ethicolegal constraints still impede the real-world implementation of data models. TECHNICAL DETAILS: The EuCanImage is an international consortium aimed at developing AI algorithms for precision medicine in oncology and enabling secondary use of the data based on necessary ethical approvals. The use of well-defined clinical data standards to allow interoperability was a central element within the initiative. The consortium is focused on 3 different cancer types and addresses 7 unmet clinical needs. We have conceived and implemented an innovative process to capture clinical data from hospitals, transform it into the newly developed EuCanImage data models, and then store the standardized data in permanent repositories. This new workflow combines recognized software (REDCap for data capture), data standards (FHIR for data structuring), and an existing repository (EGA for permanent data storage and sharing), with newly developed custom tools for data transformation and quality control purposes (ETL pipeline, QC scripts) to complement the gaps.
conclusionThis article synthesizes our experience and procedures for health care data interoperability, standardization, and reproducibility.
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Registered trials
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.