Lung Cancer Screening With Low-Dose CT: Evidence, AI Integration, and Implementation Challenges
This Lancet review synthesises evidence from the NLST and NELSON trials confirming mortality reduction with low-dose CT lung cancer screening, and examines key optimisation questions including risk model-based patient selection, personalised screening intervals, novel biomarkers, and AI-driven nodule detection. The authors highlight opportunities to integrate cardiovascular and COPD assessments into screening workflows, making the case for AI and biomarker-enhanced screening programmes as the next frontier in early cancer detection.
The original study
Lung cancer screening.
- Authors
- Adams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ
- Journal
- Lancet (London, England)
- Type
- Journal Article, Review
- PMID
- 36563698
Original abstract
Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.