Screening saves lives - but not immediately. Low-dose CT (LDCT) screening for lung cancer reduces lung cancer deaths in high-risk individuals, as demonstrated by multiple randomized clinical trials. However, the benefit takes years to appear. In the meantime, screening carries immediate risks: false-positive results leading to unnecessary procedures, procedural complications, radiation exposure, anxiety, and overdiagnosis.
A fundamental tension in preventive medicine. People in poor health who are unlikely to live long enough to experience the eventual mortality benefit from screening are still exposed to all of its immediate harms. Screening an individual who will die of another cause before lung cancer ever threatens them offers no benefit but real risk. Yet most clinical guidelines offer vague or inconsistent guidance on how poor health or limited life expectancy should influence the screening decision.
Time to benefit: a quantifiable solution. The 'time to benefit' (TTB) is defined as the delay from when screening begins to when a measurable reduction in cancer deaths first appears. If a patient's life expectancy is less than the TTB, they are statistically unlikely to live long enough to gain from screening. Calculating TTB from clinical trial data provides clinicians with an evidence-based threshold to guide individualized screening recommendations.
No prior TTB estimate for lung cancer screening. TTB analysis has been applied to breast cancer and colorectal cancer screening, but no prior study had calculated TTB specifically for lung cancer screening. This study set out to fill that gap by systematically reviewing and meta-analyzing all major randomized trials of LDCT screening, yielding the first evidence-based TTB estimate for this intervention.
Systematic identification of eligible trials. The researchers searched major medical databases to identify randomized controlled trials of lung cancer screening with low-dose CT that reported lung cancer mortality outcomes. After applying inclusion and exclusion criteria - including excluding trials with high risk of bias - eight trials involving 88,526 participants were included in the final pooled analysis.
Reconstructing survival data from published figures. Most trials did not provide individual patient data, so the team used specialized software (DigitizeIt) to extract numerical data points from published survival curve figures. These extracted data were then reconstructed into individual-level time-to-event datasets using established statistical methods, enabling consistent analysis across all trials.
Weibull curves and Bayesian simulation. For each trial, the researchers fitted Weibull survival curves separately for the screened and unscreened groups. They then ran 10,000 Markov chain Monte Carlo (MCMC) simulations to generate probability distributions for the absolute risk reduction at each time point - a method that accounts for uncertainty in the data and produces reliable confidence intervals around the TTB estimates.
Three benefit thresholds defined. Rather than reporting a single TTB number, the researchers calculated TTB at three different levels of absolute risk reduction: one death prevented per 500 screened persons (a high benefit threshold), one per 1,000 (a moderate threshold), and one per 2,000 (a minimal benefit threshold). This range allows clinicians to tailor decisions based on how much benefit a patient or their physician considers meaningful.
The headline result. At the moderate benefit threshold - one lung cancer death prevented per 1,000 people screened - the pooled time to benefit was 3.4 years (95% confidence interval: 2.2 to 5.1 years). This means that, on average across all included trials, 3.4 years of screening must elapse before this level of population benefit is achieved.
Results at other thresholds. At the minimal benefit threshold of one death prevented per 2,000 screened persons, the TTB was 2.2 years. At the high benefit threshold of one death prevented per 500 persons, the TTB was 5.2 years. This graduated range confirms that the TTB is sensitive to how much benefit is considered sufficient to justify screening.
Benefit grows with longer follow-up. The cumulative benefit of screening increased with time. At 5 years of screening, approximately 1.9 lung cancer deaths were prevented per 1,000 people screened. This increased to 4.9 deaths prevented per 1,000 at 10 years, confirming that the absolute benefit of lung cancer screening continues to accumulate well beyond the minimum TTB threshold.
Consistent results across all trials. Heterogeneity across the eight included studies was very low (I2 = 0%) for all three benefit thresholds, indicating that the TTB estimate was highly consistent across different countries, study designs, and patient populations. This consistency strengthens confidence in the robustness of the 3.4-year estimate.
A minimum life expectancy threshold of 3.4 years. The primary clinical implication of this study is that lung cancer screening is most beneficial for individuals with a life expectancy greater than 3.4 years - the time it takes to prevent one death per 1,000 screened. Patients who are unlikely to live at least this long are exposed to the immediate harms of screening without a realistic chance of living to experience the mortality benefit.
Patients with very short life expectancy clearly should not be screened. The results demonstrate that for individuals with a life expectancy of less than 2.2 years, the harms of screening almost certainly outweigh any achievable benefit. Yet surveys show that some primary care physicians consider recommending lung cancer screening even for patients with life expectancies as short as 6 months, often relying on subjective clinical judgment rather than evidence-based tools.
Lung cancer screening TTB is shorter than for other cancers. Comparing these results with previous TTB analyses for breast and colorectal cancer screening is illuminating. Breast cancer screening via mammography and colorectal cancer screening via stool blood testing each require approximately 10 years before one death per 1,000 screened is prevented. The 3.4-year TTB for lung cancer screening is dramatically shorter, suggesting it may be more appropriate for older adults with limited but meaningful life expectancy.
A tool for shared decision-making. Rather than serving as a hard cutoff, the TTB estimates in this study are intended to support individualized conversations between clinicians and patients. By providing evidence-based numbers at multiple thresholds, clinicians can tailor the screening discussion to each patient's life expectancy, health priorities, and tolerance for immediate risks - replacing subjective judgment with transparent, quantified evidence.
Trial data may not perfectly reflect real-world settings. The included trials enrolled participants healthy enough to be eligible for lung cancer surgery, making the study populations somewhat healthier than average. Real-world data suggest that procedural complication rates after screening are higher outside trial settings, which could alter the risk-benefit balance and change the practical TTB.
Predominantly male populations. Several of the included trials enrolled predominantly or exclusively male participants. Because most real-world lung cancer screening occurs in men, the results are broadly applicable, but generalizability to women, younger people at risk, racial and ethnic minorities, and those with occupational exposures to lung carcinogens is uncertain.
Individual-level data would enable finer estimates. The current analysis relied mostly on survival curves extracted from published figures rather than individual patient data. Access to individual-level data would enable stratified analyses by sex, age, smoking history, comorbidity burden, and other factors that likely affect the absolute benefit of screening - providing more precise TTB estimates for specific subgroups.
Practical tools for life expectancy estimation already exist. Clinicians do not need to estimate life expectancy subjectively. The ePrognosis platform (which includes tools like the Lee Index), the LYFS-CT model specifically designed for lung cancer screening decisions, and the CAN score used within the Veterans Affairs system are all validated tools that can be used alongside this TTB analysis to guide individualized, evidence-based screening recommendations.