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Persistent Identifier
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doi:10.5683/SP2/BEMODM |
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Publication Date
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2021-05-19 |
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Citation Date
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Title
| Data from: Assessment of plasma proteomics biomarker’s ability to distinguish benign from malignant lung nodules |
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Other Identifier
| Dryad: doi:10.5061/dryad.qc264m2 |
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Author
| Silvestri, Gerard A.
Tanner, Nichole T. (University of South Carolina)
Kearney, Paul
Vachani, Anil (University of Pennsylvania)
Massion, Pierre P.
Porter, Alexander
Springmeyer, Steven C.
Fang, Kenneth C.
Midthun, David
Mazzone, Peter J.
Madtes, D. (University of Washington)
Landis, J. (University of Pennsylvania)
Levesque, A. (McGill University)
Rothe, K. (University of British Columbia)
Balaan, M. (Allegheny General Hospital)
Dimitt, B. (Allegheny General Hospital)
Fortin, B. (Brigham and Women's Hospital)
Ettinger, N. (Children's Hospital)
Pierre, A. (Johns Hopkins University)
Yarmus, L. (Johns Hopkins University)
Oakjones-Burgess, K. (Johns Hopkins University)
Desai, N. (Johns Hopkins University)
Hammoud, Z. (Lebanese University)
Sorenson, A. (Minnesota Department of Agriculture)
Murali, R. (New York Medical College)
Pass, H. (New York University)
Lackey, A. (Rutgers University)
Carter, L. (University of Manchester)
King, S. (University of Melbourne)
Kuo, E. (University of Arkansas System)
Jacques, L. (University of California System)
Hong, G. (University of Macau)
Henderson, M. (University of Queensland)
Lamberti, J. (University of Rochester)
Balekian, A. (University of Southern California)
Allison, F. (University of Toronto) |
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Point of Contact
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Use email button above to contact.
UBC Library Research Data Team |
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Description
| Abstract
Background: Lung nodules are a diagnostic challenge, with an estimated yearly incidence of 1.6 million in the United States. This study evaluated the accuracy of an integrated proteomic classifier in identifying benign nodules in patients with a pretest probability of cancer (pCA) ≤ 50%. Methods: A prospective, multicenter observational trial of 685 patients with 8- to 30-mm lung nodules was conducted. Multiple reaction monitoring mass spectrometry was used to measure the relative abundance of two plasma proteins, LG3BP and C163A. Results were integrated with a clinical risk prediction model to identify likely benign nodules. Sensitivity, specificity, and negative predictive value were calculated. Estimates of potential changes in invasive testing had the integrated classifier results been available and acted on were made. Results: A subgroup of 178 patients with a clinician-assessed pCA ≤ 50% had a 16% prevalence of lung cancer. The integrated classifier demonstrated a sensitivity of 97% (CI, 82-100), a specificity of 44% (CI, 36-52), and a negative predictive value of 98% (CI, 92-100) in distinguishing benign from malignant nodules. The classifier performed better than PET, validated lung nodule risk models, and physician cancer probability estimates (P < .001). If the integrated classifier results were used to direct care, 40% fewer procedures would be performed on benign nodules, and 3% of malignant nodules would be misclassified. Conclusions: When used in patients with lung nodules with a pCA ≤ 50%, the integrated classifier accurately identifies benign lung nodules with good performance characteristics. If used in clinical practice, invasive procedures could be reduced by diverting benign nodules to surveillance. Trial Registry: ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov). (2020-06-30)
Usage notes
Data File Supporting PublicationData Flatfile.xlsx
(2020-06-30) |
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Subject
| Other |
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Related Publication
| Article doi: https://doi.org/10.1016/j.chest.2018.02.012 |
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Notes
| Dryad version number: 1
Version status: submitted
Dryad curation status: Published
Sharing link: https://datadryad.org/stash/share/UIljfvFP1w7rWs76G8sc8VKIqzeN19Ci74DrQ4F0CwY
Storage size: 106162
Visibility: public |
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Distribution Date
| 2018-05-16 |
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Deposit Date
| 2020-06-30 |
