联合临床特征、双能量CT影像学特征及定量参数分析肺结节良恶性鉴别的危险因素，构建预测模型，分析碘图定量参数对肺结节定性诊断中的意义。

选择2015年1月至2021年6月我院收治的经双能量CT(DECT)检查≤3 cm的肺结节844例为对象，以病理结果为金标准分为良性组181例，恶性组673例，采用SPSS 23.0分析获取定性诊断的危险预测因子。Logistic回归多因素分析评估组间关系；ROC曲线评估模型的诊断价值。

844例有872个符合条件的肺结节，良性组182个肺结节、恶性组690个结节，单因素分析提示年龄、CT值、RECIST直径、碘浓度、碘比值、性别、吸烟史、结节数、密度、空洞征、含气支气管征在良恶性结节鉴别中有统计学差异(P<0.05)，碘浓度≥1.05 mg/ml(AUC＝0.632，灵敏度＝77.4%、特异度＝45.1%)、碘比值≥13.9%(AUC＝0.604，灵敏度＝89.9%、特异度＝29.1%)时肺结节倾向于恶性。Logistic回归分析显示RECIST直径、碘浓度、密度、空泡征、含气支气管征被纳入预测模型，ROC曲线提示AUC＝0.808(Cut-off值＝0.49，灵敏度＝81.4%、特异度＝67.6%)，去除碘图定量参数后重新构建的预测模型ROC曲线(AUC＝0.802，P＝0.000，Cut-off值＝0.481，灵敏度＝79.4%、特异度＝68.7%)。

年龄、CT值、RECIST直径、碘浓度、碘比值、性别、吸烟史、结节数、密度、空洞征、含气支气管征为肺结节定性诊断的危险预测因素，碘浓度≥1.05 mg/ml、碘比值≥13.9%时肺结节倾向于恶性；构建预测模型具有诊断意义，碘浓度在整体模型中贡献较小。

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.