Evidence map›Paper›PMID 24979231›Full record

ArticlePloS one2014

Comparing benefits from many possible computed tomography lung cancer screening programs: extrapolating from the National Lung Screening Trial using comparative modeling.

Pamela M McMahon, Rafael Meza, Sylvia K Plevritis, William C Black, C Martin Tammemagi, Ayca Erdogan, Kevin ten Haaf, William Hazelton, Theodore R Holford, Jihyoun Jeon and 10 more

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

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

  1. Guideline
  2. Guideline
  3. Lung Cancer Incidence and Mortality with Extended Follow-up in the National Lung Screening Trial.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2019
    Trial
  4. Lung cancer detectability by test, histology, stage, and gender: estimates from the NLST and the PLCO trials.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2015
    Trial
  5. Review
  6. Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Birth Cohort‒Specific Smoking Patterns by Family Income in the U.S.American journal of preventive medicine · 2023
    Article
  11. Article
  12. Article
  13. Cancer screening simulation models: a state of the art review.BMC medical informatics and decision making · 2021
    Review
  14. Article
  15. Article
  16. Article
  17. Evaluating Discrimination of a Lung Cancer Risk Prediction Model Using Partial Risk-Score in a Two-Phase Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
    Article
  18. Article
  19. Article
  20. 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

20 authors.

Pamela M McMahonInstitute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America; Department of Radiology, Harvard Medical School, Boston, Massachusetts, United States of America.
Rafael MezaDepartment of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America.
Sylvia K PlevritisDepartment of Radiology, Stanford University, Stanford, California, United States of America.
William C BlackDepartment of Radiology, Dartmouth Medical School, Hanover, New Hampshire, United States of America.
C Martin TammemagiDepartment of Community Health Sciences, Brock University, Ontario, Canada.
Ayca ErdoganDepartment of Radiology, Stanford University, Stanford, California, United States of America.
Kevin ten HaafDepartment of Public Health, Erasmus MC, Rotterdam, Netherlands.
William HazeltonProgram of Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Theodore R HolfordDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.
Jihyoun JeonDepartment of Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Lauren ClarkeCornerstone Systems Northwest, Inc., Lynden, Washington, United States of America.
Chung Yin KongInstitute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America; Department of Radiology, Harvard Medical School, Boston, Massachusetts, United States of America.
Sung Eun ChoiInstitute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Vidit N MunshiInstitute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Summer S HanDepartment of Radiology, Stanford University, Stanford, California, United States of America.
Joost van RosmalenDepartment of Public Health, Erasmus MC, Rotterdam, Netherlands.
Paul F PinskyDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, United States of America.
Suresh MoolgavkarDepartment of Epidemiology, School of Public Health University of Washington, Seattle, Washington, United States of America, and Department of Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Harry J de KoningDepartment of Public Health, Erasmus MC, Rotterdam, Netherlands.
Eric J FeuerDivision of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, United States of America.

Funding

Yale Pathology Tissue Services Shared ResourceP30CA016359 · NCI · YALE UNIVERSITY · PI MEGAN C KING · 1985 to 2026
$85.0M
Yale University Clinical and Translational Science Award ProgramUL1TR000142 · NCATS · YALE UNIVERSITY · PI SHERWIN, ROBERT S · 2012 to 2015
$31.8M
Comparative Modeling of Lung Cancer Control PoliciesU01CA152956 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI DE KONING, HARRY J, KONG, CHUNG YIN · 2010 to 2014
$7.4M
Applications of Multi-Criteria Optimization (AMCO) to Cancer Simulation ModelingK25CA133141 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI KONG, CHUNG YIN · 2009 to 2013
$844k
NCATS NIH HHS UL1 TR000142NCI NIH HHS K25 CA133141NCI NIH HHS U01 CA152956
6 · The paper itself

Abstract

backgroundThe National Lung Screening Trial (NLST) demonstrated that in current and former smokers aged 55 to 74 years, with at least 30 pack-years of cigarette smoking history and who had quit smoking no more than 15 years ago, 3 annual computed tomography (CT) screens reduced lung cancer-specific mortality by 20% relative to 3 annual chest X-ray screens. We compared the benefits achievable with 576 lung cancer screening programs that varied CT screen number and frequency, ages of screening, and eligibility based on smoking. METHODS AND

findingsWe used five independent microsimulation models with lung cancer natural history parameters previously calibrated to the NLST to simulate life histories of the US cohort born in 1950 under all 576 programs. 'Efficient' (within model) programs prevented the greatest number of lung cancer deaths, compared to no screening, for a given number of CT screens. Among 120 'consensus efficient' (identified as efficient across models) programs, the average starting age was 55 years, the stopping age was 80 or 85 years, the average minimum pack-years was 27, and the maximum years since quitting was 20. Among consensus efficient programs, 11% to 40% of the cohort was screened, and 153 to 846 lung cancer deaths were averted per 100,000 people. In all models, annual screening based on age and smoking eligibility in NLST was not efficient; continuing screening to age 80 or 85 years was more efficient.

conclusionsConsensus results from five models identified a set of efficient screening programs that include annual CT lung cancer screening using criteria like NLST eligibility but extended to older ages. Guidelines for screening should also consider harms of screening and individual patient characteristics.

Indexed as

AdultAgedAged, 80 and overClinical Trials as TopicFemaleHumansLung NeoplasmsMaleMiddle AgedModels, StatisticalPredictive Value of TestsTomography, X-Ray Computed

Identifiers

PMID24979231
PMCPMC4076275

What OpenQuestion holds

Textmetadata
LicenceCC0
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.