Mapping lung cancer risk factors is a complex challenge that requires a large amount of data and a deep understanding of the biological, environmental, and social factors that influence the disease.
Using the OMOP standard will help address these challenges by enabling researchers to share data and knowledge more efficiently. Furthermore, OMOP supports a wide variety of data sources, allowing researchers to integrate data from different disciplines to gain a more comprehensive understanding of lung cancer risk factors.
BILBOMÁTICA, together with its partners, is working on the development of a software platform called the LUCIA Software Ecosystem to enable the integration, aggregation, and use of heterogeneous lung cancer data from cohorts and electronic health records, as well as epidemiological and environmental geospatial data sources. The LUCIA project is being analyzed and built using a design based on principles of security, privacy, ethics, and a data management plan.
It is important to note that almost 20% of lung cancer patients are "non-smokers" or "never smokers." Aside from smoking, there are several risk factors and determinants for development that can help explain its progression: increasing age (most people are 65 or older) ; indoor and outdoor pollutants, such as radon, asbestos, beryllium, arsenic, silica, nickel compounds, and chromium compounds; underlying health conditions, family history, lung diseases, and cancers of the head and neck and esophagus; genetic expression, which accounts for 8% of cases; and differences in biological pathways. While the mechanisms by which smoking contributes to the formation and progression of lung cancer are relatively clear, the biological pathways by which other risk factors contribute are less so.
For these reasons, there is an urgent need to establish assessment tools for lung cancer risk factors and its subcategories, including less common forms, to better understand the disease. This would allow for better-targeted public health policies toward more effective diagnostic pathways.
The LUCIA project receives funding from the European Union's Horizon Europe research and innovation programme under grant agreement 101096473 .