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- W3032207297 abstract "ObjectiveTo analyze retrospectively the clinical data of solitary pulmonary nodules and summarize the clinical characteristics to provide evidences for the diagnosis of benign and malignant solitary pulmonary nodules.Methods148 cases of solitary pulmonary nodules were selected in the Northern Jiangsu People′s Hospital from January 2016 to June 2018.All the lesions of these patients were confirmed by surgery, bronchoscopy or lung puncture and had definite pathological diagnosis.The clinical data were collected to judge benign and malignant solitary pulmonary nodules, including gender, age, smoking history, cancer history, family history of cancer, maximum diameter of nodule, location, lobulation, spicule, pleural indentation, vascular convergence sign, tumor markers.Single factor binary regression was performed in the univariate analysis and logistic regression in multivariate analysis of these clincial data.Results148 cases collected included 40 benign nodules and 108 malignant nodules.The univariate analysis showed that there were statistical differences in gender, age, cancer history, maximum diameter of nodule, location, lobulation, spicule, pleural indentation, vascular convergence sign between benign and malignant pulmonary nodules (all P<0.05). Multivariate logistic regression analysis showed that there were statistical differences in gender, age, maximum diameter of nodule and vascular convergence sign between benign and malignant pulmonary nodules (all P<0.05).ConclusionsPatient gender, age, maximum diameter of nodule, vascular convergence sign are independent predictors of malignance in patients with solitary pulmonary nodules.Key words: Solitary pulmonary nodule; Adenocarcinoma; Risk factors; Logistic regression analysis" @default.
- W3032207297 created "2020-06-05" @default.
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- W3032207297 date "2019-07-05" @default.
- W3032207297 modified "2023-09-24" @default.
- W3032207297 title "Clinical characteristics analysis of 148 cases of solitary pulmonary nodules" @default.
- W3032207297 doi "https://doi.org/10.3760/cma.j.issn.1673-436x.2019.13.005" @default.
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