Objective:

2

To analyze the prdictors of solitary pulmonary nodules（SPN） and establish a mathematical prediction model to verify the accuracy of the model and compare the diagnostic efficiency with the classical models，in order to improve the accuracy of non-invasive diagnosis of early lung cancer.

Methods:

2

A total of 522 SPN patients treated by The Second Affiliated Hospital of Chongqing Medical University from January 2014 to January 2021 were selected and divided into case group（432 cases） and validation group（90 cases），and their clinical and CT imaging features were retrospectively analyzed to screen out independent predictors of malignant SPN and establish a clinical prediction model. The validation group data were inserted into the model for verification，the diagnostic efficacy was compared with the Mayo model and Peking University model，and the receiver operating characteristic（ROC） curve was drawn.

Results:

2

Binary Logistic analysis showed that upper lobe，spiculation，lobulation，vascular aggregation sign，unclear boundary and the maximum diameter of nodules were independent risk factors for benign and malignant SPN. The established prediction model was

P

=

e

x

/（1+

e

x

），

X

=-3.742+（0.185×the maximum diameter of nodule）+（1.423×spiculation）+（1.143×lobulation）+（3.783×vascular aggregation sign）+（2.526×unclear boundary）+（0.730×upperlobe）. The area under ROC curve（AUC） of our model（0.875） was significantly higher than that of the Mayo model（0.776） and Peking University model（0.779）（

P

<

0.05）. It was suggested that the diagnostic efficiency of this model might be better than that of Mayo model and Peking University model.

Conclusion:

2

Upper lobe，spiculation，lobulation，vascular aggregation sign，unclear boundary and the maximum diameter of nodules are independent risk factors for benign and malignant SPN，and the diagnostic efficiency of our prediction model might be better than that of Mayo model and Peking University model，which is of great significance for the diagnosis of early lung cancer.