Evidence map›Paper›PMID 30821827›Full record

Trial reportJAMA network open2019

Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial.

Martin C Tammemägi, Kevin Ten Haaf, Iakovos Toumazis, Chung Yin Kong, Summer S Han, Jihyoun Jeon, John Commins, Thomas Riley, Rafael Meza

2 registry-linked trialsAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JAMA network open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 50 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 3 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.

NCT00047385 phase3completednot on this map

National Lung Screening Trial A Randomized Trial Comparing Low-dose Helical CT With Chest Xray for Lung Cancer

TypeinterventionalSponsorNational Cancer Institute (NCI)Ran2002 to 2010Enrolled53,454ConditionsLung CancerArmslow-dose helical computed tomography, chest radiography
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
3 · Its place in the literature

Who cites it

50 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Guideline
  4. Article
  5. Article
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  10. Review
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  14. Article
  15. 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
  16. Lung cancer screening: where do we stand?Breathe (Sheffield, England) · 2024
    Review
  17. Article
  18. Review
  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

9 authors.

Martin C TammemägiDepartment of Health Sciences, Brock University, St Catharines, Ontario, Canada.
Kevin Ten HaafDepartment of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
Iakovos ToumazisDepartment of Radiology, Stanford University School of Medicine, Palo Alto, California.
Chung Yin KongInstitute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston.
Summer S HanDepartment of Medicine, Stanford University School of Medicine, Palo Alto, California.
Jihyoun JeonSchool of Public Health, University of Michigan, Ann Arbor.
John ComminsInformation Management Systems, Rockville, Maryland.
Thomas RileyInformation Management Systems, Rockville, Maryland.
Rafael MezaDepartment of Epidemiology, University of Michigan, Ann Arbor.

Funding

Translational Oncology Research Program (Project-005)P30CA124435 · NCI · STANFORD UNIVERSITY · PI Vittorio Sebastiano · 2007 to 2026
$71.4M
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
NCI NIH HHS P30 CA124435NCI NIH HHS U01 CA199284
6 · The paper itself

Abstract

Importance: Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals. Objectives: To develop and validate a risk prediction model that incorporates low-dose computed tomography screening results. Design, Setting, and Participants: A logistic regression risk model was developed in National Lung Screening Trial (NLST) Lung Screening Study (LSS) data and was validated in NLST American College of Radiology Imaging Network (ACRIN) data. The NLST was a randomized clinical trial that recruited participants between August 2002 and April 2004, with follow-up to December 31, 2009. This secondary analysis of data from the NLST took place between August 10, 2013, and November 1, 2018. Included were LSS (n = 14 576) and ACRIN (n = 7653) participants who had 3 screens, adequate follow-up, and complete predictor information. Main Outcomes and Measures: Incident lung cancers occurring 1 to 4 years after the third screen (202 LSS and 96 ACRIN). Predictors included scores from the validated PLCOm2012 risk model and Lung CT Screening Reporting & Data System (Lung-RADS) screening results. Results: Overall, the mean (SD) age of 22 229 participants was 61.3 (5.0) years, 59.3% were male, and 90.9% were of non-Hispanic white race/ethnicity. During follow-up, 298 lung cancers were diagnosed in 22 229 individuals (1.3%). Eight result combinations were pooled into 4 groups based on similar associations. Adjusted for PLCOm2012 risks, compared with participants with 3 negative screens, participants with 1 positive screen and last negative had an odds ratio (OR) of 1.93 (95% CI, 1.34-2.76), and participants with 2 positive screens with last negative or 2 negative screens with last positive had an OR of 2.66 (95% CI, 1.60-4.43); when 2 or more screens were positive with last positive, the OR was 8.97 (95% CI, 5.76-13.97). In ACRIN validation data, the model that included PLCOm2012 scores and screening results (PLCO2012results) demonstrated significantly greater discrimination (area under the curve, 0.761; 95% CI, 0.716-0.799) than when screening results were excluded (PLCOm2012) (area under the curve, 0.687; 95% CI, 0.645-0.728) (P < .001). In ACRIN validation data, PLCO2012results demonstrated good calibration. Individuals who had initial negative scans but elevated PLCOm2012 six-year risks of at least 2.6% did not have risks decline below the 1.5% screening eligibility criterion when subsequent screens were negative. Conclusions and Relevance: According to this analysis, some individuals with elevated risk scores who have negative initial screens remain at elevated risks, warranting annual screening. Positive screens seem to increase baseline risk scores and may identify high-risk individuals for continued screening and enrollment into clinical trials. Trial Registration: ClinicalTrials.gov Identifier: NCT00047385.

Indexed as

Early Detection of CancerLung NeoplasmsRisk AssessmentFemaleHumansMaleMiddle AgedModels, StatisticalReproducibility of ResultsRisk FactorsTomography, X-Ray Computed

Identifiers

PMID30821827
PMCPMC6484623

What OpenQuestion holds

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LicenceCC BY
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.