Evidence map›Paper›PMID 34383299›Full record

ArticleCancer2021

A risk-based framework for assessing real-time lung cancer screening eligibility that incorporates life expectancy and past screening findings.

Iakovos Toumazis, Oguzhan Alagoz, Ann Leung, Sylvia K Plevritis

Abstract read
In one paragraph

Article in Cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
–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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Updates in Lung Cancer Screening: A Decade of Evidence.Seminars in respiratory and critical care medicine · 2025
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025
    Article
  10. Impact of Comorbidities on the Mortality Benefits of Lung Cancer Screening: A Post-Hoc Analysis of the PLCO and NLST Trials.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2025
    Article
  11. Review
  12. Article
  13. Article
  14. The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Iakovos ToumazisDepartment of Biomedical Data Science, Stanford University, Stanford, California.ORCID 0000-0002-3462-2137
Oguzhan AlagozDepartment of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin.
Ann LeungDepartment of Radiology, Stanford University, Stanford, California.
Sylvia K PlevritisDepartment of Biomedical Data Science, Stanford University, Stanford, California.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Comparative Modeling of Lung Cancer Prevention and Control PoliciesU01CA199284 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2015 to 2019
$8.4M
Personalized, Dynamic Risk-based Lung Cancer ScreeningF32CA220961 · NCI · STANFORD UNIVERSITY · PI TOUMAZIS, IAKOVOS · 2017 to 2018
$116k
NCI NIH HHS F32 CA220961NCI NIH HHS P30 CA016672NCI NIH HHS U01 CA199284
6 · The paper itself

Abstract

backgroundCurrent lung cancer risk-based screening approaches use a single risk-threshold, disregard life-expectancy, and ignore past screening findings. We address these limitations with a comprehensive analytical framework, the individualized lung cancer screening decision (ENGAGE) tool that aims to optimize lung cancer screening for US ever-smokers under dynamic risk assessment by incorporating life expectancy and past screening findings over time.

methodsENGAGE employs a partially observable Markov decision process framework that integrates published risk prediction and disease progression models, to dynamically assess the trade-off between the expected health benefits and harms associated with screening. ENGAGE evaluates lung cancer risk annually and provides real-time screening eligibility that maximizes the expected quality-adjusted life-years (QALYs) of ever-smokers. We compare ENGAGE against the 2013 U.S. Preventive Services Task Force (USPSTF) lung cancer screening guideline and single-threshold risk-based screening paradigms.

resultsCompared with the 2013 USPSTF guidelines, ENGAGE expands screening coverage among ever-smokers (ENGAGE: 78%, USPSTF: 61%), while reducing the number of screening examinations per person (ENGAGE:10.43, USPSTF:12.07, P < .001), yields higher effectiveness in terms of increased lung cancer-specific mortality reduction (ENGAGE: 19%, USPSTF: 15%, P < .001) and improves screening efficiency (ENGAGE: 696, USPSTF: 819 screens per death avoided, P < .001). When compared against a single-threshold risk-based screening strategy, ENGAGE increases QALY requiring 30% fewer screens per death avoided (ENGAGE: 696, single-threshold: 889, P < .001), and reduces false positives by 40%.

conclusionsENGAGE provides a comprehensive framework for dynamic risk-based assessment of lung cancer screening eligibility by incorporating life expectancy and past screening findings that can serve to guide future policies on the effectiveness and efficiency of screening. LAY SUMMARY: A novel decision-analytical screening framework was developed for lung cancer, the individualized lung cancer screening decision (ENGAGE) tool to provide personalized screening schedules for ever-smokers. ENGAGE captures the dynamic nature of lung cancer risk and incorporates life expectancy into the screening decision-making process. ENGAGE integrates past screening findings and changes in smoking behavior of individuals and provides informed screening decisions that outperform existing screening guidelines and single-threshold risk-based screening approaches. A personalized lung cancer screening program facilitated by a tool such as ENGAGE could enhance the efficiency of lung cancer screening.

Indexed as

Early Detection of CancerLung NeoplasmsHumansLife ExpectancyMass ScreeningRisk Assessmentlife expectancylow-dose computed tomographylung cancerlung cancer riskmedical decision-makingpartially observable Markov decision process (POMDP)personalized risk assessmentrisk-based screeningrisk factorsrisk predictionscreeningsmokinguncertainty

Identifiers

PMID34383299
PMCPMC8578300

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.