Background
cancers, coronary calcification, cancer, Lung cancer, NELSON, emphysema, lung cancer, deaths
CANCERS, CANCER, LUNG CANCER, EMPHYSEMA, LUNG CANCER
Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer-related deaths worldwide in 2020 with an estimated 2.20 million diagnosed cases and 1.79 million deaths per year [In NELSON, NLST, and other trials, all participants were regarded to be at high risk for lung cancer, but only a low percentage was diagnosed with lung cancer (0.7–2.2% at baseline). This has sparked discussions that if a cohort is invited for screening based on age and long-term smoking alone (like in NELSON), many individuals will not benefit. They may either have a low-risk for lung cancer despite their age and smoking history, or may have insufficient life expectancy to benefit from screening [A drawback of NLST is the percentage of false positives. Over three screening rounds, 24.2% of CT scans were considered positive (lung nodule ≥ 4 mm diameter), but 96.4% of those were false-positives. The approach of the NELSON study, based on nodule volumetry and an intermediate test result category, led to a lower percentage of false positive results. Nevertheless, in NELSON, still one fifth of the participants had indeterminate or suspicious lung nodules at baseline [In recent years, there has been an increasing recognition of the utility of risk models [There is great interest in evaluating new data sources to improve models for participant selection and nodule management [Technological advances have enabled the development of risk prediction models for lung cancer using multi-source data. Not only genetic and environmental data derived from the collected NELSON data, but also artificial intelligence (AI) based nodule evaluation, and chest CT imaging biomarkers beyond lung nodules may enrich prediction models and improve individual selection and lung nodule management.The goal of this project is to improve the efficiency of lung cancer screening through expanding and integrating further NELSON data, addressing the limitation of previous approaches and developing multi-source risk prediction models for personalized risk assessment and lung nodule stratification. Therefore this project has the following objectives, namely to:
Construct and optimize polygenic risk scores for prediction of lung cancer.Construct and evaluate air pollution-based environmental risk scores for prediction of lung cancer.Determine the cancer probability of lung nodules using an AI risk score.Measure CT biomarkers beyond lung nodules in NELSON screening rounds (emphysema, coronary calcification, bone density, vertebral height, body composition).Develop and validate multisource data prediction models for selecting participants at the highest risk of lung cancer.Develop and validate multisource data prediction models for lung nodule management with the aim to reduce the number of unnecessary follow up screenings and referrals.Evaluate the cost-effectiveness of the newly developed prediction models.