Evidence map›Paper›PMID 31942534›Full record

ArticleJNCI cancer spectrum2019

Cost-Effectiveness Analysis of Lung Cancer Screening Accounting for the Effect of Indeterminate Findings.

Iakovos Toumazis, Emily B Tsai, S Ayca Erdogan, Summer S Han, Wenshuai Wan, Ann Leung, Sylvia K Plevritis

Open access · goldAbstract read
In one paragraph

Article in JNCI cancer spectrum, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 4 pooled it
3.0field-weighted citation impact, top 8% 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

17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025
    Article
  7. Article
  8. Article
  9. Risk Stratification in Cost-Effectiveness Analyses of Cancer Screening: Intervention Eligibility, Strategy Choice, and Optimality.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Direct reconstruction of anatomical change in low-dose lung nodule surveillance.Journal of medical imaging (Bellingham, Wash.) · 2021
    Article
  15. [Research Progress on Lung Cancer Screening].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2020
    Review
  16. Article
  17. 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

7 authors at 1 institution in 1 country.

Iakovos ToumazisDepartments of Radiology and of Biomedical Data Science.ORCID 0000-0002-3462-2137
Emily B TsaiDepartment of Radiology, Stanford University, Stanford, CA.
S Ayca ErdoganDepartment of Radiology, Stanford University, Stanford, CA.
Summer S HanStanford Center for Biomedical Informatics Research, Departments of Medicine and Neurosurgery.
Wenshuai WanDepartment of Radiology, Stanford University, Stanford, CA.
Ann LeungDepartment of Radiology, Stanford University, Stanford, CA.
Sylvia K PlevritisDepartments of Radiology and of Biomedical Data Science.
Stanford University · US

Funding

Translational Oncology Research Program (Project-005)P30CA124435 · NCI · STANFORD UNIVERSITY · PI Melinda L. Telli · 2007 to 2026
$71.4M
Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
Personalized, Dynamic Risk-based Lung Cancer ScreeningF32CA220961 · NCI · STANFORD UNIVERSITY · PI TOUMAZIS, IAKOVOS · 2017 to 2018
$116k
NCATS NIH HHS UL1 TR003142NCI NIH HHS F32 CA220961NCI NIH HHS P30 CA124435
6 · The paper itself

Abstract

backgroundNumerous health policy organizations recommend lung cancer screening, but no consensus exists on the optimal policy. Moreover, the impact of the Lung CT screening reporting and data system guidelines to manage small pulmonary nodules of unknown significance (a.k.a. indeterminate nodules) on the cost-effectiveness of lung cancer screening is not well established.

methodsWe assess the cost-effectiveness of 199 screening strategies that vary in terms of age and smoking eligibility criteria, using a microsimulation model. We simulate lung cancer-related events throughout the lifetime of US-representative current and former smokers. We conduct sensitivity analyses to test key model inputs and assumptions.

resultsThe cost-effectiveness efficiency frontier consists of both annual and biennial screening strategies. Current guidelines are not on the frontier. Assuming 4% disutility associated with indeterminate findings, biennial screening for smokers aged 50-70 years with at least 40 pack-years and less than 10 years since smoking cessation is the cost-effective strategy using $100 000 willingness-to-pay threshold yielding the highest health benefit. Among all health utilities, the cost-effectiveness of screening is most sensitive to changes in the disutility of indeterminate findings. As the disutility of indeterminate findings decreases, screening eligibility criteria become less stringent and eventually annual screening for smokers aged 50-70 years with at least 30 pack-years and less than 10 years since smoking cessation is the cost-effective strategy yielding the highest health benefit.

conclusionsThe disutility associated with indeterminate findings impacts the cost-effectiveness of lung cancer screening. Efforts to quantify and better understand the impact of indeterminate findings on the effectiveness and cost-effectiveness of lung cancer screening are warranted.

Identifiers

PMID31942534
PMCPMC6947892
OpenAlexW2945599151

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.