Abstract: Objective: To establish a predicting model using multivariate logistic regression analysis for estimating the probability of malignancy in solitary pulmonary nodules, and to compare our model with Mayo model and Brock model. Methods: From January 2015 to December 2017, 319 patients with SPNs identified by histopathology in Shenzhen peoples' hospital were analyzed retrospectively. Among 319 cases, 229 patients were in modeling group (group A), and 90 patients were in validating group (group B). We analyzed gender, age, diameter, smoking history, spiculation, upper location, unclear border, lobulation, vacuole sign, vessel convergence sign, pleural indentation, ground glass opacity and calcification in patients of group A, selected independent influencing factors by univariate analysis and multivariate logistic regression analysis and established a predicting model. Our model was verified with the date of group B, and was compared with Mayo model and Brock model. Results: The age, diameter, upper location, unclear border, lobulation, vacuole sign, vessel convergence sign, pleural indentation, and ground glass opacity were shown statistically significance between malignant and benign SPNs in univariate analysis (P<0.05). The spiculation, unclear border, lobulation, and ground glass opacity were independent influencing factors in multivariate logistic regression analysis. When group B data was substituted into the established formula, the area under the ROC curve was 0.894, sensitivity was 91.3%, specificity was 77.3%, positive likely ratio was 4.02, negative likely ratio was 0.11, positive predictive value was 80.8%, negative predictive was 89.5%. The difference between our model and Mayo model was statistically significant (P=0.0049). The difference between our model and Brock model was not statistically significant (P=0.79). Conclusion: The spiculation, unclear border, lobulation, and ground glass opacity are independent influencing factors between benign and malignant solitary pulmonary nodules. This logistic regression equation has favorable effective functions for the diagnosis of SPNs. For patients in this study, our model is better than Mayo model, and is same as Brock model.