Introduction Europe is an important focus for compiling accurate and up-to-date world cancer statistics owing to its large share of the world's total cancer burden. This article presents incidence and mortality estimates for 25 major cancers across 40 individual countries within European areas and the European Union (EU-27) for the year 2020. Methods The estimated national incidence and mortality rates are based on statistical methodology previously applied and verified using the most recently collected incidence data from 151 population-based cancer registries, mortality data and 2020 population estimates. Results Estimates reveal 4 million new cases of cancer (excluding non-melanoma skin cancer) and 1.9 million cancer-related deaths. The most common cancers are: breast in women (530,000 cases), colorectum (520,000), lung (480,000) and prostate (470,000). These four cancers account for half the overall cancer burden in Europe. The most common causes of cancer deaths are: lung (380,000), colorectal (250,000), breast (140,000) and pancreatic (130,000) cancers. In EU-27, the estimated new cancer cases are approximately 1.4 million in males and 1.2 million in females, with over 710,000 estimated cancer deaths in males and 560,000 in females. Conclusion The 2020 estimates provide a basis for establishing priorities in cancer-control measures across Europe. The long-established role of cancer registries in cancer surveillance and the evaluation of cancer control measures remain fundamental in formulating and adapting national cancer plans and pan-European health policies. Given the estimates are built on recorded data prior to the onset of coronavirus disease 2019 (COVID-19), they do not take into account the impact of the pandemic.

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.

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Background: Long-term projections of cancer burden are an important input to health-policy planning. Major cancer risk factors e such as genetics, lifestyle, environmental exposure to carcinogens, in combination with the associated population structures e impact on observed number of cancer incidence and mortality cases. The latest estimates on cancer burden in the EU and EFTA countries for the year 2020 predicted 2.8 million new cancer cases and 1.3 million cancer deaths. This study investigates how these numbers would change up to the year 2040 under different population structures, assuming the estimated 2020 cancer incidence and mortality crude rates remain the same.Methods: Using the estimated 2020 cancer incidence and mortality crude rates and the projected populations for years 2025, 2030, 2035 and 2040, as released by Eurostat, cancer incidence and mortality was projected by age, sex, country, and cancer site. Alternative projections were computed using different population structures, also released by Eurostat, modelled on the following demographic scenarios: 20% lower fertility rate, lower mortality rate, 33% lower migration rate, nomigration and 33% higher migration rate as compared to the 2020 baseline. Results:The number of new cancer cases in the EU and EFTA countries is estimated to increase to 3.4 million by 2040, with an increment of 21.4%. The increment is estimated to be highest in the scenario with lower mortality (23.3%), while it is lowest for the no-migration scenario (19.7%). Large variability is estimated among the different cancer sites, varying from a decrease of 5.2% for testicular cancer up to an increase of 35.2% for the mesothelioma in males. The variability is highest for the no-migration and lower mortality scenarios. The number of cancer deaths in the EU and EFTA countries is estimated to grow to 1.7 million by 2040, with an increment of 32.2%, increasing up to 35.4% for the lower mortality scenario. Conclusions:The results obtained are of big value in planning health policies for cancer in the EU and EFTA region.Legal entity responsible for the study: The authors.

Population-based cancer registry data provide a key epidemiological resource for monitoring cancer in defined populations. Validation of the data variables contributing to a common data set is necessary to remove statistical bias; the process is currently performed centrally. An ontology-based approach promises advantages in devolving the validation process to the registry level but the checks regarding multiple primary tumours have presented a hurdle. This work presents a solution by modelling the international rules for multiple primary cancers in description logic. Topography groupings described in the rules had to be further categorised in order to simplify the axioms. Description logic expressivity was constrained as far as possible for reasons of automatic reasoning performance. The axioms were consistently able to trap all the different types of scenarios signalling violation of the rules. Batch processing of many records were performed using the Web Ontology Language application programme interface. Performance issues were circumvented for large data sets using the software interface to perform the reasoning operations on the basis of the axioms encoded in the ontology. These results remove one remaining hurdle in developing a purely ontology-based solution for performing the European harmonised data-quality checks, with a number of inherent advantages including the formalisation and integration of the validation rules within the domain data model itself.