Lung Cancer Screening in High-Risk Populations: Current Evidence, Implementation Challenges, and Future Directions
Lung cancer is the leading cause of cancer-related mortality worldwide, yet the majority of cases are diagnosed at advanced stages when a cure is rarely achievable. Low-dose computed tomography (LDCT) screening in high-risk populations reduces lung cancer mortality by 20–24% in randomised trials, but fewer than 20% of eligible adults in the United States undergo annual screening. We conducted a structured narrative review of PubMed, Embase, and the Cochrane Library for studies published between January 2002 and March 2026, supplemented by review of current clinical guidelines from the USPSTF, NCCN, ACS, CHEST, and ERS. Evidence from the National Lung Screening Trial (NLST) and the NELSON trial establishes the mortality benefit of LDCT screening, though both trials have important methodological limitations that affect generalisability. The NLST predominantly detected non-small cell lung cancer (NSCLC), particularly adenocarcinoma and squamous cell carcinoma, while small cell lung cancer (SCLC) was infrequently screen detected and showed no survival benefit from early detection. Guideline eligibility criteria have progressively broadened, and multivariable risk model-based selection using the PLCOm2012 now demonstrates superiority over categorical smoking thresholds in prospective validation cohorts, with the added benefit of reducing racial and ethnic eligibility disparities. Overdiagnosis estimates have declined substantially with extended follow-up, reaching 7% when observation exceeds five years. Implementation remains critically deficient: patient stigma, provider knowledge gaps, structural barriers, and inadequate electronic health record infrastructure collectively account for screening uptake below 20%. Integrating smoking cessation into screening encounters is evidence-based and cost-effective. Artificial intelligence tools show promising performance in nodule detection and risk prediction, but lack the prospective external validation required for routine clinical deployment. The field has established efficacy; the urgent challenge is now effectiveness at scale. Transitioning to risk model-based eligibility, expanding access to underserved populations, and mandating cessation integration represent the three highest-priority actions. A research agenda addressing never-smoker screening, personalised intervals, and robust AI validation must proceed in parallel.