Skip Navigation
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.


The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Your Environment. Your Health.

Publication Detail

Title: Validation of a multiprotein plasma classifier to identify benign lung nodules.

Authors: Vachani, Anil; Pass, Harvey I; Rom, William N; Midthun, David E; Edell, Eric S; Laviolette, Michel; Li, Xiao-Jun; Fong, Pui-Yee; Hunsucker, Stephen W; Hayward, Clive; Mazzone, Peter J; Madtes, David K; Miller, York E; Walker, Michael G; Shi, Jing; Kearney, Paul; Fang, Kenneth C; Massion, Pierre P

Published In J Thorac Oncol, (2015 Apr)

Abstract: Indeterminate pulmonary nodules (IPNs) lack clinical or radiographic features of benign etiologies and often undergo invasive procedures unnecessarily, suggesting potential roles for diagnostic adjuncts using molecular biomarkers. The primary objective was to validate a multivariate classifier that identifies likely benign lung nodules by assaying plasma protein expression levels, yielding a range of probability estimates based on high negative predictive values (NPVs) for patients with 8 to 30 mm IPNs.A retrospective, multicenter, case-control study was performed using multiple reaction monitoring mass spectrometry, a classifier comprising five diagnostic and six normalization proteins, and blinded analysis of an independent validation set of plasma samples.The classifier achieved validation on 141 lung nodule-associated plasma samples based on predefined statistical goals to optimize sensitivity. Using a population based nonsmall-cell lung cancer prevalence estimate of 23% for 8 to 30 mm IPNs, the classifier identified likely benign lung nodules with 90% negative predictive value and 26% positive predictive value, as shown in our prior work, at 92% sensitivity and 20% specificity, with the lower bound of the classifier's performance at 70% sensitivity and 48% specificity. Classifier scores for the overall cohort were statistically independent of patient age, tobacco use, nodule size, and chronic obstructive pulmonary disease diagnosis. The classifier also demonstrated incremental diagnostic performance in combination with a four-parameter clinical model.This proteomic classifier provides a range of probability estimates for the likelihood of a benign etiology that may serve as a noninvasive, diagnostic adjunct for clinical assessments of patients with IPNs.

PubMed ID: 25590604 Exiting the NIEHS site

MeSH Terms: Aged; Algorithms*; Biomarkers, Tumor/blood*; Female; Humans; Lung Neoplasms/blood*; Lung Neoplasms/classification; Lung Neoplasms/diagnosis; Male; Middle Aged; Multiple Pulmonary Nodules/blood*; Multiple Pulmonary Nodules/classification; Multiple Pulmonary Nodules/diagnosis; Proteomics/methods*; ROC Curve; Retrospective Studies

to Top