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    A prediction model based on biomarkers and clinical characteristics for detection of lung cancer in pulmonary nodules

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    Author
    Ma, J.
    Guarnera, M.A.
    Zhou, W.
    Date
    2017
    Journal
    Translational Oncology
    Publisher
    Translational Oncology Editorial Office
    Type
    Article
    
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    See at
    https://doi.org/10.1016/j.tranon.2016.11.001
    Abstract
    Lung cancer early detection by low-dose computed tomography (LDCT) can reduce the mortality. However, LDCT increases the number of indeterminate pulmonary nodules (PNs), whereas 95% of the PNs are ultimately false positives. Modalities for specifically distinguishing between malignant and benign PNs are urgently needed. We previously identified a panel of peripheral blood mononucleated cell (PBMC)-miRNA (miRs-19b-3p and -29b-3p) biomarkers for lung cancer. This study aimed to evaluate efficacy of integrating biomarkers and clinical and radiological characteristics of smokers for differentiating malignant from benign PNs. We analyzed expression of 2 miRNAs (miRs-19b-3p and -29b-3p) in PBMCs of a training set of 137 individuals with PNs. We used multivariate logistic regression analysis to develop a prediction model based on the biomarkers, radiographic features of PNs, and clinical characteristics of smokers for identifying malignant PNs. The performance of the prediction model was validated in a testing set of 111 subjects with PNs. A prediction model comprising the two biomarkers, spiculation of PNs and smoking pack-year, was developed that had 0.91 area under the curve of the receiver operating characteristic for distinguishing malignant from benign PNs. The prediction model yielded higher sensitivity (80.3% vs 72.6%) and specificity (89.4% vs 81.9%) compared with the biomarkers used alone (all P <.05). The performance of the prediction model for malignant PNs was confirmed in the validation set. We have for the first time demonstrated that the integration of biomarkers and clinical and radiological characteristics could efficiently identify lung cancer among indeterminate PNs. Copyright 2016 The Authors.
    Sponsors
    This work was supported in part by NCI R21CA205746, VA Merit Award I01 CX000512, award from the Geaton and JoAnn DeCesaris Family Foundation (F.J.), LUNGevity/Upstage Foundation Early Detection Award, University of Maryland Cancer Epidemiology Alliance Seed Grant, and UMD-UMB Research and Innovation Seed Grant (F.J.).
    Keyword
    Lungs--Cancer
    Tomography, X-Ray Computed
    Multiple Pulmonary Nodules
    MicroRNAs
    Logistic Models
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011928243&doi=10.1016%2fj.tranon.2016.11.001&partnerID=40&md5=e896a393fcff3b7095b06610f5d34d2c; http://hdl.handle.net/10713/11305
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.tranon.2016.11.001
    Scopus Count
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    UMB Open Access Articles 2017

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