Background: Lung cancer (LC) is the leading cause of cancer mortality and the most common cancer globally. Evidence from meta-analyses of randomized trials suggest that screening heavy smokers by low-dose computed tomography (LDCT) can reduce lung cancer mortality by approximately 20%. However, selecting heavy smokers most likely to benefit from screening is challenging. 
Methods: We evaluated the discrimination performance of established risk models and trial criteria in predicting lung cancer incidence and mortality in a large cohort of screening-age adults (1-2). In subsequent studies (3-6), based on participants from the UK Biobank, German ESTHER study and Norwegian HUNT cohorts, we assessed whether the addition of blood-based biomarkers to the risk prediction models may enhance selection of those at highest risk for lung cancer. 
Results: In the proteomics-based research, incorporating a protein marker model to the PLCOm2012 risk model increased the AUC by 0.057 and resulted in up to 16 percentage point higher sensitivity to identify future LC cases as compared to the LDCT trial criteria, demonstrating improved selection of high-risk individuals for screening (3). In an epigenomics study, it was demonstrated that by adding AHRR (cg05575921) or F2RL3 (cg03636183) methylation the proportion of missed lung cancer cases could be reduced by up to 68% without increasing the screening load compared to the criteria employed in the LDCT trials (4). In ongoing research, we are evaluating the potential of metabolites and other DNA methylation markers for short-term and long-term lung cancer risk prediction (6). Further optimizations are planned through implementation of a multi-omics strategy to integrate diverse molecular layers with evaluation of expected impact of enhanced selection of participants on a population level.
Conclusion: Enhanced, biomarker-supported eligibility criteria could strongly improve the effectiveness and efficiency of LDCT screening by better targeting high-risk individuals.


1.    Frick C, Seum T, Bhardwaj M, Holland-Letz T, Schottker B, Brenner H. Head-to-head comparisons of risk discrimination by questionnaire-based lung cancer risk prediction models: a systematic review and meta-analysis. EClinicalMedicine. 2025;80:103075.
2.    Bhardwaj M, Schöttker B, Holleczek B, Brenner H. Comparison of discrimination performance of 11 lung cancer risk models for predicting lung cancer in a prospective cohort of screening-age adults from Germany followed over 17 years. Lung Cancer. 2022;174:83-90.
3.    Bhardwaj M, Frick C, Schottker B, Holleczek B, Brenner H. Next-generation proteomics improves lung cancer risk prediction. Mol Oncol. 2025.
4.    Bhardwaj M, Schöttker B, Holleczek B, Brenner H. Enhanced selection of people for lung cancer screening using AHRR (cg05575921) or F2RL3 (cg03636183) methylation as biological markers of smoking exposure. Cancer Commun (Lond). 2023;43(8):956-959.
5.    Yu H, Raut JR, Bhardwaj M, et al. A serum microRNA signature for enhanced selection of people for lung cancer screening. Cancer Commun (Lond). 2022;42(11):1222-1225.
6.     Bhardwaj M, Sun Y.Q., Frick C, Schöttker B, et al. Blood-based DNA methylation marker model for short-term and long-term lung cancer risk prediction. (Unpublished).