ArticleRadiology2008
Estimating long-term effectiveness of lung cancer screening in the Mayo CT screening study.
Article in Radiology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- All-cause mortality versus cancer-specific mortality as outcome in cancer screening trials: A review and modeling study.Cancer medicine · 2019Pooled it
- International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2011Pooled it
- Trial
- Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025Article
- Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.CPT: pharmacometrics & systems pharmacology · 2025Review
- Deep Q-networks with web-based survey data for simulating lung cancer intervention prediction and assessment in the elderly: a quantitative study.BMC medical informatics and decision making · 2022Article
- Review
- Association of invitation to lung cancer screening and tobacco use outcomes in a VA demonstration project.Preventive medicine reports · 2019Article
- A Decision Analysis of Follow-up and Treatment Algorithms for Nonsolid Pulmonary Nodules.Radiology · 2019Article
- Review
- Smoking and Lung Cancer Mortality in the United States From 2015 to 2065: A Comparative Modeling Approach.Annals of internal medicine · 2018Article
- Benefits and harms of lung cancer screening in HIV-infected individuals with CD4+ cell count at least 500 cells/μl.AIDS (London, England) · 2018Article
- Population impact of lung cancer screening in the United States: Projections from a microsimulation model.PLoS medicine · 2018Article
- Evaluating lung cancer screening in China: Implications for eligibility criteria design from a microsimulation modeling approach.PloS one · 2017Article
- Article
- Article
- Targeted screening of individuals at high risk for pancreatic cancer: results of a simulation model.Radiology · 2015Article
- Article
- Histoplasmosis mimicking primary lung neoplasm.BMJ case reports · 2014Article
- Article
Corrections and comments
- Commented on by
Authors and funding
9 authors.
Funding
Abstract
purposeTo use individual-level data provided from the single-arm study of helical computed tomographic (CT) screening at the Mayo Clinic (Rochester, Minn) to estimate the long-term effectiveness of screening in Mayo study participants and to compare estimates from an existing lung cancer simulation model with estimates from a different modeling approach that used the same data. MATERIALS AND
methodsThe study was approved by institutional review boards and was HIPAA compliant. Deidentified individual-level data from participants (1520 current or former smokers aged 50-85 years) in the Mayo Clinic helical CT screening study were used to populate the Lung Cancer Policy Model, a comprehensive microsimulation model of lung cancer development, screening findings, treatment results, and long-term outcomes. The model predicted diagnosed cases of lung cancer and deaths per simulated study arm (five annual screening examinations vs no screening). Main outcome measures were predicted changes in lung cancer-specific and all-cause mortality as functions of follow-up time after simulated enrollment and randomization.
resultsAt 6-year follow-up, the screening arm had an estimated 37% relative increase in lung cancer detection, compared with the control arm. At 15-year follow-up, five annual screening examinations yielded a 9% relative increase in lung cancer detection. The relative reduction in cumulative lung cancer-specific mortality from five annual screening examinations was 28% at 6-year follow-up (15% at 15 years). The relative reduction in cumulative all-cause mortality from five annual screening examinations was 4% at 6-year follow-up (2% at 15 years).
conclusionScreening may reduce lung cancer-specific mortality but may offer a smaller reduction in overall mortality because of increased competing mortality risks associated with smoking.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.