Multivariate Analysis of Solid Pulmonary Nodules Smaller than 1 cm in Distinguishing Lung Cancer from Intrapulmonary Lymph Nodes

Background Preoperative diagnosis and differential diagnosis of small solid pulmonary nodules are very difficult. Computed tomography (CT), as a common method for lung cancer screening, is widely used in clinical practice. The aim of this study was to analyze the clinical data of patients with malignant pulmonary nodules and intrapulmonary lymph nodes in the clinical diagnosis and treatment of Methods Patients with solid pulmonary nodules who underwent surgery from June 2017 to June 2020 were analyzed retrospectively. The clinical data of 145 nodules (lung adenocarcinoma 60, lung carcinoid 2, malignant mesothelioma 1, sarcomatoid carcinoma 1, lymph node 81) were collected and finally divided into two groups: lung adenocarcinoma and intrapulmonary lymph nodes, and their clinical data were statistically analyzed. According to the results of univariate analysis (χ² test, t test), the variables with statistical differences were selected and included in Logistic regression multivariate analysis. The predictive variables were determined and the receiver operating characteristic (ROC) curve was drawn to get the area under the curve (AUC) value of the area under the curve. Results Logistic regression analysis showed that the longest diameter, Max CT value, lobulation sign and spiculation sign were important indicators for distinguishing lung adenocarcinoma from intrapulmonary lymph nodes, and the risk ratios were 106.645 (95%CI: 3.828-2,971.220, P Conclusions For