Broad scientific aims that can be addressed through the consortium include:
Biomarkers of lung cancer risk. Extensive pilot data strongly supports the use of biomarkers in lung cancer risk prediction models. Within Project 2 we will systematically evaluate a comprehensive panel of promising risk biomarkers using pre- diagnostic blood samples. This will result in a selected panel of biomarkers of lung cancer risk that will be incorporated in risk prediction models, and used to identify those subjects most likely to benefit from CT- screening.
An integrated risk prediction model to identify individuals at high risk of lung cancer, initially analyzing epidemiological and smoking related phenotypes in the first years but then integrating targeted biomarker, genomic profile, and lung function data applied to LC CT screening populations with a total of 950 CT-detected LC patients with biosamples from 46,057 screening individuals (including 9,759 in Canada, 26,722 in NLST, and 9,576 in Europe. The clinical utility of the models will be assessed by net benefit and decision curve analysis. As a result of these analyses we will (4) develop a risk calculator for use in clinical settings. Improving personalized risk assessment for breast cancer, overall and by clinically relevant subtypes, by integrating polygenic information into risk models for risk-based prevention and screening.
A comprehensive LC probability models for individuals with LDCT-detected non-calcified pulmonary nodules. In this aim we will (a) first establish the 2Ddiameter-based probability model in N. American CT programs based on 36,481 participants, and then externally validate it based on 9,576 participants in the European LDCT programs; (b) establish the volume3D and radiomics-based probability model in European CT programs based on 9,576 participants in European CT programs, and then externally validate it in the North American CT screening populations; and (c) assess the added predictive value and clinical usefulness of targeted genomic and molecular profiles in both the 2D diameter- and 3D and radiomics volume-based LC probability models based on risk stratification table analysis and decision curve analysis. Finally we will (d) compare the model performance with the existing classification system such as Lung-RADS.