Low-Dose CT lung cancer screening:clinical evidence and implementation research

Lung cancer causes more deaths than breast, cervical, and colorectal cancer combined. Nevertheless, population-based lung cancer screening is still not considered standard practice in most countries worldwide. Early lung cancer detection leads to better survival outcomes: patients diagnosed with stage 1A lung cancer have a >75% 5-year survival rate, compared to < 5% at stage 4. LDCT thorax imaging for the secondary prevention of lung cancer has been studied at length, and has been shown to significantly reduce lung cancer mortality in high-risk populations. The US national lung screening trial reported 20% overall reduction in lung cancer mortality when comparing LDCT to chest x-ray, and the NELSON trial more recently reported 24% reduction when comparing LDCT to no screening. Hence, the focus has now shifted to implementation research. Consequently, the 4-IN-THE-LUNG-RUN consortium, based in 5 European countries, has set up a large-scale multi-center implementation trial. Successful implementation and accessibility of low-dose CT lung cancer screening are dependent on many factors, not limited to; population selection, recruitment strategy, CT-screening frequency, lung nodule management, participant compliance and cost-effectiveness. This review provides an overview of current evidence for LDCT lung cancer screening, and draws attention to major factors which need to be addressed to successfully implement standardized, effective, and accessible screening throughout Europe. Evidence shows that through the appropriate use of risk-prediction models and a more personalized approach to screening, efficacy could be improved. Further, extending the screening interval for low-risk individuals to reduce costs and associated harms is a possibility, and through the use of volumetric based measurement and follow-up, false positive results can be greatly reduced. Finally, smoking cessation programs could be a valuable addition to screening programs and artificial intelligence could offer the solution to the added workload pressures Radiologists are facing.