Objective: To investigate the value of quantitative analysis from spectral CT imaging and radiomics analysis from iodine overlay maps in differentiating inflammatory and malignant pulmonary lesions. Methods: A total of 123 patients with solitary pulmonary nodules underwent contrast enhanced spectral CT scan in arterial phase and were divided into two groups (38 cases in inflammatory group and 85 cases in malignant group). The Gemstone Spectral Imaging (GSI) viewer was used for image display and data analysis. The parameters, including CT value on 70 keV and 40keV monochromatic images, slope of spectrum energy curve (λ), iodine concentration (IC), effective-Z (Zeff) value, normalized iodine concentration (NIC) normalized effective-Z (NZeff) value and the maximum diameter of lesion (D) were measured. After the multivariate logistic regression analysis, the individual diagnostic performance of independent factor was compared by receiver operating characteristic (ROC) curve. Radiomics features were extracted from manually segmented ROIs. Patients were randomized to training or test set using a 7:3 ratio. Pearson’s correlation and Recursive Feature Elimination (RFE) were used to select features. A radiomics-based predictive model using Linear Discriminant Analysis was developed and calibrated with fivefold cross-validation. ROC curve was generated to assess the diagnostic performance. Results: CT value on 40keV monochromatic image, λ100-70 keV, λ70-40 keV, λ100-40 keV, IC, NIC, Zeff, NZeff and D showed significant differences between inflammatory and malignant lesions (P<0.05). NIC was an independent factor based on the multivariate logistic regression analysis (P<0.001). The area under the curve (AUC) value of NIC was 0.728. Ten radiomic features were selected for predicting malignant lesions. The AUC value of model (0.843) was significantly higher than NIC (P=0.034). Conclusion: Spectral CT with GSI mode helps to identify inflammatory and malignant pulmonary lesions. The radiomics-based predictive model provides a more promising tool for differentiating.