Evidence map›Paper›PMID 38737731›Full record

ArticleCHEST pulmonary2024

The Lung Cancer Prediction Model "Stress Test": Assessment of Models' Performance in a High-Risk Prospective Pulmonary Nodule Cohort.

Brent E Heideman, Michael N Kammer, Rafael Paez, Terra Swanson, Caroline M Godfrey, See-Wei Low, David Xiao, Thomas Z Li, Jacob R Richardson, Michael A Knight and 5 more

Abstract read
In one paragraph

Article in CHEST pulmonary, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

15 authors.

Brent E HeidemanSection of Pulmonary, Critical Care, Allergy and Immunologic Diseases, Atrium Health Wake Forest Baptist, Winston-Salem, NC.
Michael N KammerDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.
Rafael PaezDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.
Terra SwansonDepartment of Medicine, Vanderbilt University Medical Center, Nashville, TN.
Caroline M GodfreyDepartment of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN.
See-Wei LowDivision of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, OH.
David XiaoDepartment of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN.
Thomas Z LiDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN.
Jacob R RichardsonDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.
Michael A KnightDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.
Samira ShojaeeDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.
Stephen A DeppenDepartment of Surgery, Tennessee Valley Healthcare System, Veterans Affairs, Nashville, TN; and the Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN.
Robert J LentzDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.
Eric L GroganDepartment of Surgery, Tennessee Valley Healthcare System, Veterans Affairs, Nashville, TN; and the Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN.
Fabien MaldonadoDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.

Funding

The Vanderbilt Institute for Clinical and Translational Research (VICTR)UL1TR000445 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BERNARD, GORDON RAPHAEL · 2012 to 2016
$41.4M
Validation of Biomarkers of Risk for the Early Detection of Lung CancerU01CA152662 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI DEPPEN, STEPHEN, GROGAN, ERIC L · 2010 to 2025
$12.8M
Novel Integrative Approach for the Early Detection of Lung Cancer using Repeated MeasuresR01CA253923 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI LANDMAN, BENNETT A., MALDONADO, FABIEN · 2021 to 2025
$3.4M
NCATS NIH HHS UL1 TR000445NCI NIH HHS R01 CA253923NCI NIH HHS U01 CA152662
6 · The paper itself

Abstract

backgroundPulmonary nodules represent a growing health care burden because of delayed diagnosis of malignant lesions and overtesting for benign processes. Clinical prediction models were developed to inform physician assessment of pretest probability of nodule malignancy but have not been validated in a high-risk cohort of nodules for which biopsy was ultimately performed. RESEARCH QUESTION: Do guideline-recommended prediction models sufficiently discriminate between benign and malignant nodules when applied to cases referred for biopsy by navigational bronchoscopy? STUDY DESIGN AND

methodsWe assembled a prospective cohort of 322 indeterminate pulmonary nodules in 282 patients referred to a tertiary medical center for diagnostic navigational bronchoscopy between 2017 and 2019. We calculated the probability of malignancy for each nodule using the Brock model, Mayo Clinic model, and Veterans Affairs (VA) model. On a subset of 168 patients who also had PET-CT scans before biopsy, we also calculated the probability of malignancy using the Herder model. The performance of the models was evaluated by calculating the area under the receiver operating characteristic curves (AUCs) for each model.

resultsThe study cohort contained 185 malignant and 137 benign nodules (57% prevalence of malignancy). The malignant and benign cohorts were similar in terms of size, with a median longest diameter for benign and malignant nodules of 15 and 16 mm, respectively. The Brock model, Mayo Clinic model, and VA model showed similar performance in the entire cohort (Brock AUC, 0.70; 95% CI, 0.64-0.76; Mayo Clinic AUC, 0.70; 95% CI, 0.64-0.76; VA AUC, 0.67; 95% CI, 0.62-0.74). For 168 nodules with available PET-CT scans, the Herder model had an AUC of 0.77 (95% CI, 0.68-0.85).

interpretationCurrently available clinical models provide insufficient discrimination between benign and malignant nodules in the common clinical scenario in which a patient is being referred for biopsy, especially when PET-CT scan information is not available.

Indexed as

indeterminate pulmonary nodulelung cancernavigational bronchoscopyprediction modelsrisk assessment

Identifiers

PMID38737731
PMCPMC11087042

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.