Objective  To establish diagnostic models for pulmonary nodules based on texture features of PET/CT images and improve the identification of lung cancer.Methods  We selected 52 patients who underwent 18F-PET/CT scan from the Department of Nuclear Medicine of Capital Medical University Xuanwu Hospital，then the PET/CT images and the information of demography and medical imaging were collected .The PET/CT texture parameters were extracted by Contourlet conversion and co-occurrence matrix arithmetic.Based on which we built the SVM (support vector machine) diagnosis models and got the results of classification for each pulmonary nodule.To improve the effectiveness of diagnostic models,we also built SVM models with the parameters of nodule margin,maximum standard uptake value and halo sign.At last we evaluated the effectiveness of the diagnostic models by the index of sensitivity,specificity,accuracy,etc.Results The diagnostic model of texture features had the sensitivity of 90.7% and specificity of 93.5%.The diagnostic model based on texture plus medical imaging information had the sensitivity of 95.7% and specificity of 100.0%.Conclusions  This diagnosis models based on texture features of PET/CT images have good classification of  benign and malignant nodules.