Objective To explore the feasibility of radiomics features combined with different machine learning methods based on CT scans to predict lymphovascular and perineural invasion in patient with gastric cancer.Methods A total of 142 patients with gas-tric cancer lymphovascular confirmed by operative pathological examination were retrospectively selected.Among all patients,there were 96 positive cases and 46 negative cases.Among 137 patients with perineural invasion,there were 76 positive cases and 61 nega-tive cases.The 3D-Slicer package was used for delineation,and the Pyradiomics package was used to extract radiomics features.All data were randomly divided into training set and test set in an 8∶2 ratio.Intraclass correlation coefficient(ICC),Pearson correla-tion analysis,least absolute shrinkage and selection operator(LASSO)algorithm were used for feature selection.Support vector machine(SVM),K-nearest neighbor(KNN),decision tree(DT),random forest(RF),extreme tree(ET),extreme gradient boosting(XGBoost),and LightGBM were used to compare the models of lymphovascular and perineural invasion,respectively.Receiver operating characteris-tic(ROC)curve and area under the curve(AUC)were used to evaluate the predictive performance of these models.Results The lymphovascular group AUC of SVM,KNN,DT,RF,ET,XGBoost,and LightGBM in the training set were 0.926,0.753,1.000,0.999,1.000,1.000,and 0.917,and the AUC in the test set were 0.894,0.692,0.456,0.678,0.753,0.650,and 0.650,respectively.The perineural invasion group AUC of SVM,KNN,DT,RF,ET,XGBoost,and LightGBM in the training set were 0.864,0.794,1.000,1.000,1.000,1.000,and 0.866,and the AUC in the test set were 0.861,0.706,0.700,0.672,0.731,0.667,and 0.678,respectively.Conclusion Based on venous phase CT radiomics features combined with machine learning methods,it is feasible to predict lymphovascu-lar and perineural invasion of gastric cancer preoperatively.Among the variousmachine learning methods,SVM shows the best predictive performance for lymphovascular and perineural invasion in patient with gastric cancer.