Evidence map›Paper›PMID 40783586›Full record

ArticleScientific reports2025

Patient and nodule characteristics associated with adherence to lung cancer screening in a large integrated healthcare system.

Shuang Yang, Muxuan Liang, Hiren J Mehta, Ramzi G Salloum, Dejana Braithwaite, Yonghui Wu, Jessica Islam, Xuhong Zhang, Ya-Chen Tina Shih, Jinhai Huo and 2 more

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Shuang YangDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Muxuan LiangDepartment of Biostatistics, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA.
Hiren J MehtaDivision of Pulmonary, Critical care, and Sleep Medicine, University of Florida College of Medicine, Gainesville, FL, USA.
Ramzi G SalloumDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Dejana BraithwaiteDivision of Population Health Sciences, Department of Surgery, College of Medicine, University of Florida, Gainesville, FL, USA.
Yonghui WuDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Jessica IslamMoffitt Cancer Center, Tampa, FL, USA.
Xuhong ZhangDepartment of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, USA.
Ya-Chen Tina ShihDepartment of Radiation Oncology, School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Jinhai HuoGlobal Real World Evidence, Johnson & Johnson Innovative Medicine, Raritan, NJ, USA.
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Yi GuoDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA. yiguo@ufl.edu.

Funding

The benefits and harms of lung cancer screening in FloridaR01CA246418 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2023
$1.7M
NIH HHS R01CA246418
6 · The paper itself

Abstract

We examined the association of pulmonary nodule characteristics with adherence to follow-up low-dose computed tomography (LDCT) after the initial screening in lung cancer screening. Using 2014-2021 electronic health record data from a large integrated health system, we analyzed adherence to Lung Imaging Reporting and Data System (Lung-RADS) follow-up recommendations, considering socio-demographic, clinical factors, and natural language processing-extracted nodule characteristics. Multivariable logistic regression models assessed the impact of these factors on adherence to follow-up LDCT. Among 2,673 individuals (mean age = 66.8 ± 5.9 years), overall adherence was 27.6%, with rates of 24.2%, 27.5%, 26.7%, and 64.0% for Lung-RADS categories 1-4 A. A race-ethnicity disparity in adherence was observed among category 1, with non-Hispanic blacks less likely to adhere than non-Hispanic whites (OR[95% CI] = 0.59[0.41-0.85]). Among patients in categories 2 to 4 A, category 4 A was significantly more likely to adhere (OR[95% CI] = 3.18[1.86-5.40]) and having more nodules increased adherence (OR[95% CI] = 1.12[1.09-1.14]). Adherence to follow-up LDCT is suboptimal, driven by patient and nodule characteristics, and influenced by how physicians communicated initial CT results. These findings underscore the need for structured screening programs and consistent follow-up protocols to improve adherence and ensure effective lung cancer screening.

Indexed as

Early Detection of CancerLung NeoplasmsPatient ComplianceAgedDelivery of Health Care, IntegratedElectronic Health RecordsFemaleHumansMaleMiddle AgedTomography, X-Ray ComputedAdherenceElectronic health recordsReal-world evidence

Identifiers

PMID40783586
PMCPMC12335435

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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.