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Background Population-based cancer registries constitute an important information source in cancer epidemiology. Studies collating and comparing data across regional and national boundaries have proved important for deploying and evaluating effective cancer-control strategies. A critical aspect in correctly comparing cancer indicators across regional and national boundaries lies in ensuring a good and harmonised level of data quality, which is a primary motivator for a centralised collection of pseudonymised data. The recent introduction of the European Union’s general data-protection regulation (GDPR) imposes stricter conditions on the collection, processing, and sharing of personal data. It also considers pseudonymised data as personal data. The new regulation motivates the need to find solutions that allow a continuation of the smooth processes leading to harmonised European cancer-registry data. One element in this regard would be the availability of a data-validation software tool based on a formalised depiction of the harmonised data-validation rules, allowing an eventual devolution of the data-validation process to the local level. Results A semantic data model was derived from the data-validation rules for harmonising cancer-data variables at European level. The data model was encapsulated in an ontology developed using the Web-Ontology Language (OWL) with the data-model entities forming the main OWL classes. The data-validation rules were added as axioms in the ontology. The reasoning function of the resulting ontology demonstrated its ability to trap registry-coding errors and in some instances to be able to correct errors. Conclusions Describing the European cancer-registry core data set in terms of an OWL ontology affords a tool based on a formalised set of axioms for validating a cancer-registry’s data set according to harmonised, supra-national rules. The fact that the data checks are inherently linked to the data model would lead to less maintenance overheads and also allow automatic versioning synchronisation, important for distributed data-quality checking processes.

Background: Breast cancer is the most common cancer and the leading cause of cancer-related death in females, with a large societal and economic impact. Decisions regarding its treatment are largely affected by the categorization into different subtypes with hormone receptor status and HER2 status being the most important predictive factors. Other biological markers play an important role for prognostic and predictive reasons. The data collection and harmonization of cancer cases are performed by cancer registries whose collection of parameters largely differs, partially including results from biomarker testing. Methods: This systematic literature review consisting of a total of 729 reports determined whether information about biomarker testing in breast cancer cases is collected and published by cancer registries worldwide. Results: The number of publications using breast cancer biomarker data from registries steeply rose with the beginning of the 21st century and some hospital-based and population-based cancer registries reacted with immediate collection of biomarker data following the recommendation of clinical guidelines. For female breast cancer, biomarkers have achieved an essential clinical value and this review points to a steady increase in the collection of biomarker data by cancer registries during the last decade. Conclusions: In the future, recommendations for biomarker data collection and coding by cancer registries may be required to ensure harmonization and comparability of the data.

The aim of this study was to show that age-adjusted cancer incidence rates for an area may not be representative of the incidence in subareas. We propose a simple measure to show the amount of geographical variability. European age-standardized incidence rates (ASRs) for 'all sites excluding nonmelanoma skin cancer', for men, in 2014, for Nordic countries as a whole, for each country (Denmark, Faroe Islands, Finland, Greenland, Iceland, Sweden and Norway) and for their regions, were retrieved from the Nordcan with corresponding standard errors SEs. We compared the ASR for Nordic countries versus single country and single country versus specific regions. The overlapping of 95% confidence intervals was used for ASRs comparisons. As a measure of variability, we computed the range between the highest and the lowest ASR within an area and the ratio between this range and the ASR of the overall area, r/R=(range/ASR)×100. The 95% confidence interval of the ASR for Nordic countries as a whole did not overlap those of the majority of the single countries; in fact, the r/R - which provides a clue for the amount of underlying geographical variability - was rather large (57.1%). Within countries, the variability was negligible in Iceland (r/R=9.6%), whereas the highest value was found in Sweden (37.1%). The ASR does not provide any information on underlying geographical variability. Therefore, its interpretation could be misleading. When data for subareas are available, the r/R, which is simple to compute and to understand, should be added to the ASR for providing more truthful information.