Lung cancer screening uptake among high-risk individuals: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Low-dose computed tomography (LDCT) screening has been shown to reduce lung cancer mortality. However, uptake among high-risk individuals eligible for lung cancer screening remains low. The aim of this study was to determine the uptake and factors associated with lung cancer screening among high-risk individuals. Methods We systematically searched the Medline, Embase, and Scopus databases from inception to April 7, 2025. The protocol was registered with PROSPERO (CRD420251008323). Studies were eligible if they screened high-risk populations using LDCT and examined associated factors. Risk of bias was assessed using the Newcastle-Ottawa Scale. A random-effects model was conducted to estimate the uptake and associated factors. Results Of 6,991 potentially relevant articles, 28 studies were included (total n = 2,312,274; range: 171-1,273,013 participants). The overall pooled uptake rate was 44% (95% CI: 24-66%), with significant heterogeneity observed. Studies showed that factors associated with non-uptake included older age, current smoking, racial minority status, lower level of education, lower income, presence of comorbidities, and lack of insurance. Meta-analyses revealed that current smokers were less likely to undergo screening compared to former smokers (OR 0.72, 95% CI: 0.59-0.87; p < 0.01), and non-white participants were less likely to undergo screening compared to white participants (OR 0.72, 95% CI: 0.56-0.92; p < 0.01). For risk of bias, only six studies were rated as having ‘moderate risk,’ while the rest were rated as having low risk. Conclusions Lung cancer screening uptake remains moderate among high-risk individuals. Targeted interventions should prioritize current smokers and individuals from non-white minority groups. Lung cancer screening campaigns should be expanded more widely, especially among current smokers and minority populations, to reduce disparities in lung cancer screening. Key messages • Lung cancer screening uptake remains moderate among high-risk individuals, particularly current smokers and those of non-white minority status. • Targeted interventions should focus on current smokers and minority populations to reduce disparities in lung cancer screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".