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Identification of preoperative prediction factors of tumor subtypes for patients with solitary ground-glass opacity pulmonary nodules

By Meishuang Li, Yanan Wang, Yulong Chen and Zhenfa Zhang


Abstract Background Recent wide spread use of low-dose helical computed tomography for the screening of lung cancer have led to an increase in the detection rate of very faint and smaller lesions known as ground-glass opacity nodules. The purpose of this study was to investigate the clinical factors of lung cancer patients with solitary ground-glass opacity pulmonary nodules on computed tomography. Methods A total of 423 resected solitary ground-glass opacity nodules were retrospectively evaluated. We analyzed the clinical, imaging and pathological data and investigated the clinical differences in patient with adenocarcinoma in situ / minimally invasive adenocarcinoma and those with invasive adenocarcinoma. Results Three hundred and ninety-three adenocarcinomas (92.9%) and 30 benign nodules were diagnosed. Age, the history of family cancer, serum carcinoembryonic antigen level, tumor size, ground-glass opacity types, and bubble-like sign in chest CT differed significantly between adenocarcinoma in situ / minimally invasive adenocarcinoma and invasive adenocarcinoma (p:0.008, 0.046, 0.000, 0.000, 0.000 and 0.001). Receiver operating characteristic curves and univariate analysis revealed that patients with more than 58.5 years, a serum carcinoembryonic antigen level > 1.970 μg/L, a tumor size> 13.50 mm, mixed ground-glass opacity nodules and a bubble-like sign were more likely to be diagnosed as invasive adenocarcinoma. The combination of five factors above had an area under the curve of 0.91, with a sensitivity of 82% and a specificity of 87%. Conclusion The five-factor combination helps us to distinguish adenocarcinoma in situ / minimally invasive adenocarcinoma from invasive adenocarcinoma and to perform appropriate surgery for solitory ground-glass opacity nodules

Topics: Solitory ground-glass opacity pulmonary nodules, ROC curve, Clinical features, Five-factor combination, Pathology, Surgery, RD1-811, Anesthesiology, RD78.3-87.3
Publisher: BMC
Year: 2018
DOI identifier: 10.1186/s13019-018-0696-7
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