Evidence map›Paper›PMID 40100218›Full record

ArticleJAMA network open2025

Adherence to Annual Lung Cancer Screening and Rates of Cancer Diagnosis.

Roger Y Kim, Katharine A Rendle, Nandita Mitra, Christine Neslund-Dudas, Robert T Greenlee, Stacey A Honda, Marilyn M Schapira, Michael J Simoff, Jihyoun Jeon, Rafael Meza and 2 more

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Improving lung cancer screening diagnostic efficiency.Current opinion in pulmonary medicine · 2026
    Review
  7. Review
  8. Review
  9. Journal of health communication · 2026
    Article
  10. Article
  11. Article
  12. Article
  13. Updates in Lung Cancer Screening: A Decade of Evidence.Seminars in respiratory and critical care medicine · 2025
    Review
  14. Article
  15. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Roger Y KimDivision of Pulmonary, Allergy and Critical Care, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Katharine A RendleDepartment of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Nandita MitraDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia.
Christine Neslund-DudasDepartment of Public Health Sciences, Henry Ford Health, Detroit, Michigan.
Robert T GreenleeMarshfield Clinic Research Institute, Marshfield, Wisconsin.
Stacey A HondaCenter for Health Research, Kaiser Permanente Hawaii, Oahu.
Marilyn M SchapiraDivision of General Internal Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania.
Michael J SimoffDepartment of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, Michigan.
Jihyoun JeonDepartment of Epidemiology, University of Michigan, Ann Arbor.
Rafael MezaDepartment of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada.
Debra P RitzwollerInstitute for Health Research, Kaiser Permanente Colorado, Denver.
Anil VachaniDivision of Pulmonary, Allergy and Critical Care, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.

Funding

Translational Research Support CoreP30ES013508 · NIEHS · UNIVERSITY OF PENNSYLVANIA · PI A. Clementina Mesaros · 2006 to 2026
$35.3M
Center for Research to Optimize Precision Lung Cancer Screening in Diverse PopulationsUM1CA221939 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI RITZWOLLER, DEBRA P, VACHANI, ANIL · 2018 to 2023
$15.3M
Comparative Modeling of Lung Cancer Prevention, Early Detection and Treatment InterventionsU01CA253858 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2020 to 2025
$8.4M
Assessment of a Radiomics-Based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary NodulesK08CA279881 · NCI · UNIVERSITY OF PENNSYLVANIA · PI Roger Yeon-Kyu Kim · 2023 to 2026
$998k
NCI NIH HHS K08 CA279881NCI NIH HHS U01 CA253858NCI NIH HHS UM1 CA221939NIEHS NIH HHS P30 ES013508
6 · The paper itself

Abstract

Importance: Adherence to annual lung cancer screening (LCS) is a proposed quality metric for LCS programs, but data linking annual adherence to lung cancer outcomes are lacking. Objective: To investigate annual LCS adherence rates across 2 subsequent LCS rounds among adults undergoing baseline LCS and examine the association of adherence with lung cancer diagnosis rates. Design, Setting, and Participants: This retrospective cohort study included adults aged 55 to 75 years who formerly or currently smoked and underwent baseline LCS between January 1, 2015, and December 31, 2018, across 5 US health care systems in the Population-Based Research to Optimize the Screening Process-Lung Consortium. Participants with missing Lung Computed Tomography Screening Reporting & Data System scores or a lung cancer diagnosis prior to LCS initiation were excluded. Data were analyzed from October 2023 to October 2024. Exposures: For negative baseline screening results, T1 and T2 screening adherence was defined as chest computed tomography (CT) between 10 and 18 months and 22 and 30 months after baseline, respectively. For positive baseline screening results, T1 and T2 adherence was defined as chest CT between 11 and 21 months and 28 and 36 months after baseline, respectively. Main Outcomes and Measures: The main outcomes were annual T1 and T2 LCS adherence rates and associations between T1 and T2 screening adherence; annual incident lung cancer diagnoses in rounds T0 (0-12 months after baseline), T1 (>12 to 24 months after baseline), and T2 (>24 to 36 months after baseline); and cancer stage distribution. Results: A total of 10 170 individuals received baseline LCS (median age, 65 years [IQR, 60-69 years]; 5415 [53.2%] male). During round T1, 6141 of 10 033 eligible patients (61.2% [95% CI, 60.2%-62.2%]) were adherent, and during round T2, 5028 of 9966 eligible patients (50.5% [95% CI, 49.5%-51.4%]) were adherent. T1 adherence was significantly associated with T2 adherence (adjusted relative risk, 2.40; 95% CI, 2.06-2.79). Across 36 months of follow-up, 279 patients (2.7%; 95% CI, 2.4%-3.1%) were diagnosed with lung cancer. Incident lung cancer diagnosis rates were 1.3% (95% CI, 1.1%-1.6%), 0.7% (95% CI, 0.5%-0.8%), and 0.8% (95% CI, 0.6%-0.9%) during rounds T0, T1, and T2, respectively. Lung cancer diagnosis rates were higher among individuals who were LCS adherent vs nonadherent during both rounds T1 (59 of 6141 [1.0%; 95% CI, 0.7%-1.2%] vs 8 of 3892 [0.2%; 95% CI, 0.1%-0.4%]; P < .001) and T2 (63 of 5028 [1.3%; 95% CI, 1.0%-1.6%] vs 12 of 4938 [0.2%; 95% CI, 0.1%-0.4%]; P < .001). A greater proportion of early-stage lung cancers were diagnosed among individuals adherent to screening at T2 compared with those who were not (46 of 63 [73.0%] vs 3 of 12 [25.0%]; P = .006). Conclusions and Relevance: In this multicenter cohort study of adults undergoing LCS, screening adherence was associated with increased overall and early-stage lung cancer detection rates; however, adherence decreased annually after baseline screening, suggesting that it is an important LCS quality metric.

Indexed as

Early Detection of CancerGuideline AdherenceLung NeoplasmsMass ScreeningPatient ComplianceAgedFemaleHumansMaleMiddle AgedRetrospective StudiesTomography, X-Ray ComputedUnited States

Identifiers

PMID40100218
PMCPMC11920840

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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.