Evidence map›Paper›PMID 33783822›Full record

ReviewInternational journal of cancer2021

Personalising lung cancer screening: An overview of risk-stratification opportunities and challenges.

Kevin Ten Haaf, Carlijn M van der Aalst, Harry J de Koning, Rudolf Kaaks, Martin C Tammemägi

Erratum issued 2 registry-linked trialsAbstract readReview
In one paragraph

Review in International journal of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to 2 registered trials, which are not on this map. Cited by 41 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 4 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.

NCT06473870 not yet recruitingnot on this mapstarted 2024, after this paper: background citation

Understanding Lung Cancer Related Risk Factors and Their Impact

Typeobservational_patient_registrySponsorBiobizkaia Health Research InstituteRan2024 to 2027Enrolled6,160ConditionsLung Cancer Screening
NCT07073898 nacompletednot on this mapstarted 2025, after this paper: background citation

Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial

TypeinterventionalSponsorUniversity of UtahRan2025 to 2026Enrolled65ConditionsLung CancerArmsRepeated Text Messages (TM+), Conversational Agent (CA), Educational Video, Proactive Patient Navigation (PPN), Reactive Patient Navigation (RPN)
3 · Its place in the literature

Who cites it

41 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Guideline
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Updates in Lung Cancer Screening: A Decade of Evidence.Seminars in respiratory and critical care medicine · 2025
    Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. 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
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Kevin Ten HaafDepartment of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0001-5006-6938
Carlijn M van der AalstDepartment of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.
Harry J de KoningDepartment of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0003-4682-3646
Rudolf KaaksDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Martin C TammemägiDepartment of Health Sciences, Brock University, St. Catharines, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Randomised clinical trials have shown the efficacy of computed tomography lung cancer screening, initiating discussions on whether and how to implement population-based screening programs. Due to smoking behaviour being the primary risk-factor for lung cancer and part of the criteria for determining screening eligibility, lung cancer screening is inherently risk-based. In fact, the selection of high-risk individuals has been shown to be essential in implementing lung cancer screening in a cost-effective manner. Furthermore, studies have shown that further risk-stratification may improve screening efficiency, allow personalisation of the screening interval and reduce health disparities. However, implementing risk-based lung cancer screening programs also requires overcoming a number of challenges. There are indications that risk-based approaches can negatively influence the trade-off between individual benefits and harms if not applied thoughtfully. Large-scale implementation of targeted, risk-based screening programs has been limited thus far. Consequently, questions remain on how to efficiently identify and invite high-risk individuals from the general population. Finally, while risk-based approaches may increase screening program efficiency, efficiency should be balanced with the overall impact of the screening program. In this review, we will address the opportunities and challenges in applying risk-stratification in different aspects of lung cancer screening programs, as well as the balance between screening program efficiency and impact.

Indexed as

Early Detection of CancerHealthcare DisparitiesHumansLung NeoplasmsPrecision MedicineRandomized Controlled Trials as TopicRisk AssessmentSmokinglung cancer screeningpersonalised screeningrisk-based screening

Identifiers

PMID33783822
PMCPMC8251929

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.