Combined with clinical features, dual-energy CT imaging features and related quantitative parameters to analyze the independent risk factors for the differential diagnosis of benign and malignant pulmonary nodules and to construct a clinical predictive model, and analyze the value of quantitative parameters of iodine map in the qualitative diagnosis of pulmonary nodules.

All of 844 cases of the clinical data, imaging data and pathological results of ≤3 cm pulmonary nodules who were examined by dual energy CT (Dual-energyCT, DECT) from January 2015 to June 2021 were collected retrospectively, according to the pathological results, the patients were divided into benign group 181 cases and malignant group 673 cases. The data were statistically analyzed by SPSS 23.0, and the independent risk predictors of qualitative diagnosis were obtained by univariate analysis. The t test is used for the measurement data in accordance with the normal distribution, otherwise the nonparametric test is used, and the counting data are tested by χ² test. The independent risk factors are substituted into Logistic regression for multi-factor analysis, and the correlation analysis is used to evaluate the relationship among the indicators; the diagnostic value of the model was evaluated by ROC curve.

A total of 872 qualified pulmonary nodules were collected from 844 patients, including 182 pulmonary nodules in benign group and 690 nodules in malignant group. Univariate analysis showed that age, sex, smoking history, CT value, RECIST diameter, nodule number, density, cavity sign and gaseous bronchus sign, iodine concentration, iodine ratio were significantly different in the differential diagnosis of benign and malignant nodules (P<0.05), pulmonary nodules tend to be malignant when the iodine concentration ≥1.05 mg/ml (AUC=0.632, sensitivity=77.4%, specificity=45.1%) and the iodine ratio ≥13.9% (AUC=0.604, sensitivity=89.9%, specificity=29.1%). The independent risk factors were substituted into the binary Logistic regression analysis, which shows that RECIST diameter, iodine concentration, density, vacuole sign and gaseous bronchus sign were included in the prediction model, the ROC curve of the model indicated AUC=0.808 (Cut-off value=0.49, sensitivity=81.4%, specificity=67.6%) (P=0.000), and the ROC curve of the reconstructed prediction model (AUC=0.802, P=0.000, Cut-off value=0.481, Sensitivity=79.4%, specificity=68.7%) after removing the quantitative parameters of iodine map.

Age, sex, smoking history, CT value, RECIST diameter, nodule number, density, cavity sign and pneumobronchial sign, iodine concentration, iodine ratio were independent risk predictors for qualitative diagnosis of pulmonary nodules, and pulmonary nodules were more likely to be malignant when iodine concentration≥1.05 mg/ml and iodine ratio >13.9%. The clinical prediction model has good diagnostic value, but the contribution of iodine concentration to the whole model is small.