Computed Tomographic Screening Intervals for Patients at Moderate Risk of Lung Cancer
Bibliographic record
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".