Abstract IA19: Can biomarkers be used to improve risk prediction models on lung cancer?

Lung cancer kills over 1.6 million people every year, making it the chief cause of cancer death worldwide. The US National Lung Cancer Screening Trial (NLST) demonstrated in 2011 that screening with computed tomography (CT) scans could reduce lung cancer mortality by 20% and total mortality by 7%. As a result, the US Preventive Services Task Force (USPSTF) has recommended LDCT-screening for lung cancer in ever-smokers aged 55-80 years who have smoked 30 pack-years with no more than 15 years since quitting. However, the NLST study also highlighted several important negative aspects of CT screening in terms of morbidity associated with over-diagnosis, treatment of benign nodules, and financial costs. The study also indicated important differences in the benefit of screening in different participant groups as defined by their underlying risk of lung cancer, highlighting the urgent need to improve and implement risk prediction models when identifying those individuals that are at high risk and most likely to benefit from lung cancer screening. Concurrently, multiple research groups, including ours, have explored the hypothesis that circulating biomarkers can capture information on risk that cannot be provided with questionnaires. In particular, we have evaluated the potential of improving upon the USPSTF lung cancer screening criteria using a small panel of selected tumour-related proteins, data on which will be presented during the conference. Citation Format: Mattias Johansson. Can biomarkers be used to improve risk prediction models on lung cancer? [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA19.