Evidence map›Paper›PMID 40705331›Full record

ArticleJAMA network open2025

Computed Tomographic Screening Intervals for Patients at Moderate Risk of Lung Cancer.

Koen de Nijs, Harry J de Koning, Pianpian Cao, Maikol Diasparra, Rochelle Garner, Jihyoun Jeon, Jean H E Yong, Rafael Meza, Kevin Ten Haaf

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Koen de NijsDepartment of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
Harry J de KoningDepartment of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
Pianpian CaoDepartment of Public Health, College of Health and Human Sciences, Purdue University, West Lafayette, Indiana.
Maikol DiasparraStatistics Canada, Ottawa, Ontario, Canada.
Rochelle GarnerStatistics Canada, Ottawa, Ontario, Canada.
Jihyoun JeonDepartment of Epidemiology, University of Michigan, Ann Arbor.
Jean H E YongCanadian Partnership Against Cancer, Toronto, Ontario.
Rafael MezaBC Cancer Research Institute, Vancouver, British Columbia, Canada.
Kevin Ten HaafDepartment of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.

Funding

Comparative Modeling of Lung Cancer Prevention, Early Detection and Treatment InterventionsU01CA253858 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2020 to 2025
$8.4M
NCI NIH HHS U01 CA253858
6 · The paper itself

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.

Indexed as

Early Detection of CancerLung NeoplasmsMass ScreeningTomography, X-Ray ComputedAgedAged, 80 and overCanadaCost-Benefit AnalysisFemaleHumansMaleMiddle AgedQuality-Adjusted Life YearsRisk FactorsSmokingUnited States

Identifiers

PMID40705331
PMCPMC12290729

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

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 Socratic graph.