ArticleJAMA network open2025
Computed Tomographic Screening Intervals for Patients at Moderate Risk of Lung Cancer.
Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.Chest · 2026Article
- Geospatial Analysis of Accredited Lung Cancer Screening Facilities in Florida Reveals Suboptimal Alignment with High-Risk Populations.Cancer research communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Importance: The US Preventive Services Task Force (USPSTF) recommends annual computed tomographic (CT) screening for individuals aged 50 to 80 years at high risk of lung cancer. Other countries are issuing similar recommendations, with some opting for biennial screening to reduce the burden of screening. However, it is unknown whether benefits of annual screening can be preserved when adapting the interval to age, sex, and smoking history. Objective: To evaluate the health outcomes and costs of adaptive lung cancer screening intervals relative to annual screening. Design, Setting, and Participants: This economic evaluation used comparative modeling methods with 3 models: 2 Cancer Intervention and Surveillance Modeling Network models and the OncoSim model from the Canadian Partnership Against Cancer. Screening of the US 1965 birth cohort with adaptive intervals was evaluated according to age, sex, and smoking exposure. Simulated outcomes are recorded from 2005 to 2065 for subpopulations of 200 000 individuals with smoking history of 10 to less than 20, 20 to less than 30, and 30 or greater pack-years (PY) for each sex. This evaluation was conducted between September 19, 2023, to December 1, 2024. Exposure: Low-dose regular CT screening among those eligible per USPSTF 2021 recommendations. Main Outcomes and Measures: Strategy effectiveness was evaluated as lung cancer deaths prevented and life-years gained relative to annual screening. Screening burden is measured by the number of CT screens. To determine cost-effectiveness, quality-adjusted life-years (QALYs) gained and Surveillance, Epidemiology, and End Results- and Medicare-derived costs of treatment were calculated, as well as CT and follow-up examination costs. A willingness-to-pay (WTP) threshold of $100 000/QALY for cost-effectiveness was assumed. Results: Biennial screening at 50 to 60 years of age, followed by annual screening, reduced CT requirements while preserving most benefits. This strategy preserved 95.9% (intermodel range, 93.5%-97.5%) of lung cancer deaths prevented, compared with annual screening, with 20.6% (intermodel range, 19.3%-21.9%) fewer screens. Annual screening from 50 to 80 years of age was not cost-effective at a WTP threshold of $100 000/QALY. Cost-effective strategies varied by risk group, but all cost-effective strategies started with biennial screening and moved to annual screening at 60 years of age or a PY threshold of 30 to 40 was reached. Conclusions and Relevance: In this economic evaluation of lung cancer screening, biennial screening for participants younger than 60 years and those with less than 30 PY of smoking exposure maintained screening benefits relative to annual screening. Resource-constricted screening programs may consider adaptive intervals.
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