ArticleESMO real world data and digital oncology2025
Establishing standards: harmonising coding principles for a minimal cancer dataset in the OMOP Common Data Model.
Article in ESMO real world data and digital oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- 'Crossing borders' in data standardisation: application of OMOP CDM in an international clinical trial network in precision cancer medicine.Acta oncologica (Stockholm, Sweden) · 2026Article
- 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
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Analysing clinical information across a network poses challenges due to heterogeneity of data collection, storage and availability. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a standardised framework for clinical data, allowing network-level comparisons and the combination of data to enhance analytical power and increase research robustness. To capture specific oncology information across the Digital Oncology Network for Europe (DigiONE) using OMOP, we developed the Minimal Essential Description of Cancer (MEDOC) framework. Materials and methods: MEDOC was developed through several iterations and was then utilised in DigiONE pilot studies. This was a community-driven process, made possible by discussions to distinguish differences in hospital data and by conducting deep-dive sessions to solve specific issues in aligning source data with the MEDOC structure. Results: The initial version of MEDOC has been utilised in two DigiONE observational cancer studies to date with a further two studies in progress, and training resources including the implementation guide have been developed. Lessons learned in the development of our MEDOC to OMOP alignment include challenges in establishing diagnosis date, confirming metastasis location and tumour classification code due to granularity of available data, among other challenges specific to individual centres. Conclusion: The utility of MEDOC has been evidenced in research applications and will be continually developed in line with both learnings from centres and developments in the field of oncology. The implementation of MEDOC in line with the OMOP CDM is timely, given European initiatives to harmonise health care data systems.
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