Improving Lung Cancer Screening Selection: A Comparative Analysis of Risk Models and Traditional Criteria in a Western European General Population

Abstract

Background/Objectives: The objective of this study is to evaluate the performance of the traditional age/smoking criteria and existing risk prediction models in selecting high-risk populations for lung cancer screening from a Western European general population. Methods: Baseline data from the Dutch population-based Lifelines cohort, collected between 2006 and 2013, were linked to the Dutch cancer registry to confirm lung cancer diagnoses. Five-year lung cancer risk was estimated based on traditional age/smoking criteria (NLST, NELSON, SPSTF-2021) and risk prediction models (LLPv2, PLCOm2012, Hoggart, Bach and Shanghai-LCM). For every strategy, the number of individuals eligible was determined, and total lung cancer cases in the eligible groups versus the ineligible groups were calculated. Results: Among 139,120 participants (aged ≥18 years), 218 (0.2%) developed lung cancer within five years. Age/smoking criteria identified 2161–6295 (1.6–4.5%) participants as eligible, comprising 62–92 (28.4–42.2%) lung cancer cases. Risk prediction models identified 2372–4315 (1.7–3.1%) participants as eligible, comprising 40–85 (18.4–38.9%) lung cancer cases. Among lung cancers in ineligible groups, 46.2–59.6% occurred in individuals who formerly smoked, and 28.7–39.3% occurred in individuals who currently smoke. Additionally, 41.2–70.0% of lung cancer cases in ineligible groups were in individuals younger than 50, and 44.3–72.3% in individuals who had quit smoking > 15 years prior to diagnosis. Conclusions: In a Western European population, current lung cancer screening selection criteria resulted in identifying only 18–42% of lung cancer cases. Cases in ineligible groups predominantly concern individuals who currently smoke and are below the threshold age and individuals who quit smoking > 15 years ago, highlighting the opportunity for more personalized risk-based screening strategies to increase lung cancer detection.