Validation of a Multiprotein Plasma Classifier to Identify Benign Lung Nodules
Autor: | Xiao-Jun Li, Anil Vachani, Peter J. Mazzone, Clive Hayward, David E. Midthun, William N. Rom, Harvey I. Pass, Pui Yee Fong, Michel Laviolette, Jing Shi, Pierre P. Massion, Stephen W. Hunsucker, Paul Kearney, Eric S. Edell, Kenneth C. Fang, David K. Madtes, Michael G. Walker, York E. Miller |
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Rok vydání: | 2015 |
Předmět: |
Male
Proteomics Pulmonary and Respiratory Medicine medicine.medical_specialty Multivariate statistics Lung Neoplasms Bioinformatics Molecular diagnostic Lung nodule Biomarkers Tumor medicine Humans Lung cancer Aged Retrospective Studies Multiple Pulmonary Nodules Lung business.industry Retrospective cohort study Original Articles Biomarker Middle Aged medicine.disease 3. Good health medicine.anatomical_structure ROC Curve Oncology Cohort Biomarker (medicine) Female Translational Oncology Radiology business Classifier (UML) Algorithms |
Zdroj: | Journal of Thoracic Oncology |
ISSN: | 1556-0864 |
DOI: | 10.1097/jto.0000000000000447 |
Popis: | Introduction 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. Methods 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. Results 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. Conclusions 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. |
Databáze: | OpenAIRE |
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