Objective To observe the efficacy of gray-scale ultrasound-based radiomics for differentiating peripheral pulmonary adenocarcinoma and squamous cell carcinoma.Methods Data of 88 patients with single peripheral lung adenocarcinoma and 58 patients with single peripheral lung squamous cell carcinoma proved pathologically with puncture biopsy and clearly visualized with lung ultrasound were retrospectively analyzed.The patients were divided into training set(n=103)and test set(n=43)at the ratio of 7:3.Based on gray-scale ultrasound of training set,radiomics features associated with differential diagnosis of peripheral lung adenocarcinoma and lung squamous cell carcinoma were extracted and screened.Using 4 different classifiers,including support vector machine(SVM),linear discriminant analysis(LDA),logistic regression(LR)and the least absolute shrinkage and selection operator combined with logistic regression(LASSO-LR),4 corresponding radiomics models were obtained,and the relative best models were selected according to their performances under 10-fold cross validation.The receiver operating characteristic curves were drawn,the areas under the curve(AUC)were calculated to evaluate the differentiating efficacy of each model,and DeLong test was used for the comparison.The differentiating accuracy of models were obtained under the best cutoff value with the maximum Youden index.Results The AUC of SVM,LDA,LR and LASSO-LR radiomics models for differentiating peripheral lung adenocarcinoma and lung squamous carcinoma in test set was 0.864,0.867,0.880 and 0.844,respectively,and no significant difference was found among 4 models(all P＞0.05).Under the best cutoff value of each model,the corresponding accuracy of SVM,LDA,LR and LASSO-LR radiomics models for differentiating peripheral lung adenocarcinoma and lung squamous cell carcinoma was 86.05％,83.72％,88.37％and 86.05％,respectively.Conclusion Radiomics models based on gray-scale ultrasound could be used to differentiate peripheral lung adenocarcinoma and lung squamous cell carcinoma.