Clinical usefulness of polygenic risk scores in risk prediction models for lung cancer screening and lung nodule management

BACKGROUND: Lung cancer mortality decreases as a result of low-dose computed tomography (LDCT) screening, but suffers from low uptake and high false-positive rates. The impact of integrating genetic risk using a polygenic risk score (PRS) to optimize lung cancer screening remains underexplored. METHODS: . Multivariable Cox models, time-dependent area under the curve (AUC), and decision curve analyses (DCA) evaluated association, discrimination, and clinical net benefit. RESULTS: was 1.22. In this context, DCA indicated a modest net benefit for decision thresholds between 10% and 30%. CONCLUSION: PRS in lung cancer reveals context-dependent net benefit across studied populations. Although PRS adds limited value for determining screening eligibility, it may help reclassify borderline individuals and inform decisions regarding closer follow-up or invasive diagnostic procedures for high-risk screened groups.