Evidence map›Paper›PMID 29297005›Full record

ArticleAnnals of internal medicine2018

Risk-Targeted Lung Cancer Screening: A Cost-Effectiveness Analysis.

Vaibhav Kumar, Joshua T Cohen, David van Klaveren, Djøra I Soeteman, John B Wong, Peter J Neumann, David M Kent

Abstract read
In one paragraph

Article in Annals of internal medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 7 pooled it
10.3field-weighted citation impact, top 1% of its field
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

61 citing papers in PubMed, 7 syntheses or guidelines pooled it, 97 citations in OpenAlex.

  1. Pooled it
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  4. Pooled it
  5. Guideline
  6. Guideline
  7. Pooled it
  8. Trial
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  10. Article
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  12. Article
  13. Impact of Population Cancer Risk on the Cost-effectiveness of Multicancer Early Detection Testing in the United States.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
    Article
  14. Updates in Lung Cancer Screening: A Decade of Evidence.Seminars in respiratory and critical care medicine · 2025
    Review
  15. Article
  16. Article
  17. Article
  18. Review
  19. Review
  20. Article

1 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Vaibhav KumarTufts Medical Center, Boston, Massachusetts (V.K., J.T.C., J.B.W., P.J.N., D.M.K.).
Joshua T CohenTufts Medical Center, Boston, Massachusetts (V.K., J.T.C., J.B.W., P.J.N., D.M.K.).
David van KlaverenTufts Medical Center, Boston, Massachusetts, and Leiden University Medical Center, Leiden, the Netherlands (D.v.).
Djøra I SoetemanHarvard School of Public Health, Boston, Massachusetts (D.I.S.).
John B WongTufts Medical Center, Boston, Massachusetts (V.K., J.T.C., J.B.W., P.J.N., D.M.K.).
Peter J NeumannTufts Medical Center, Boston, Massachusetts (V.K., J.T.C., J.B.W., P.J.N., D.M.K.).
David M KentTufts Medical Center, Boston, Massachusetts (V.K., J.T.C., J.B.W., P.J.N., D.M.K.).
Tufts Medical Center · USHarvard University · US

Funding

Value of Personalized Risk InformationU01NS086294 · NINDS · TUFTS MEDICAL CENTER · PI KENT, DAVID M, NEUMANN, PETER · 2013 to 2017
$2.6M
NINDS NIH HHS U01 NS086294
6 · The paper itself

Abstract

Background: Targeting low-dose computed tomography (LDCT) for lung cancer screening to persons at highest risk for lung cancer mortality has been suggested to improve screening efficiency. Objective: To quantify the value of risk-targeted selection for lung cancer screening compared with National Lung Screening Trial (NLST) eligibility criteria. Design: Cost-effectiveness analysis using a multistate prediction model. Data Sources: NLST. Target Population: Current and former smokers eligible for lung cancer screening. Time Horizon: Lifetime. Perspective: Health care sector. Intervention: Risk-targeted versus NLST-based screening. Outcome Measures: Incremental 7-year mortality, life expectancy, quality-adjusted life-years (QALYs), costs, and cost-effectiveness of screening with LDCT versus chest radiography at each decile of lung cancer mortality risk. Results of Base-Case Analysis: Participants at greater risk for lung cancer mortality were older and had more comorbid conditions and higher screening-related costs. The incremental lung cancer mortality benefits during the first 7 years ranged from 1.2 to 9.5 lung cancer deaths prevented per 10 000 person-years for the lowest to highest risk deciles, respectively (extreme decile ratio, 7.9). The gradient of benefits across risk groups, however, was attenuated in terms of life-years (extreme decile ratio, 3.6) and QALYs (extreme decile ratio, 2.4). The incremental cost-effectiveness ratios (ICERs) were similar across risk deciles ($75 000 per QALY in the lowest risk decile to $53 000 per QALY in the highest risk decile). Payers willing to pay $100 000 per QALY would pay for LDCT screening for all decile groups. Results of Sensitivity Analysis: Alternative assumptions did not substantially alter our findings. Limitation: Our model did not account for all correlated differences between lung cancer mortality risk and quality of life. Conclusions: Although risk targeting may improve screening efficiency in terms of early lung cancer mortality per person screened, the gains in efficiency are attenuated and modest in terms of life-years, QALYs, and cost-effectiveness. Primary Funding Source: National Institutes of Health (U01NS086294).

Indexed as

Cost-Benefit AnalysisAgedEarly Detection of CancerFemaleHumansLife ExpectancyLung NeoplasmsMaleMass ScreeningMiddle AgedPatient SelectionQuality-Adjusted Life YearsRadiography, ThoracicRiskSmokersTomography, X-Ray Computed

Identifiers

PMID29297005
PMCPMC6533918
OpenAlexW2781986854

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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 OpenQuestion graph.