Abstract: As for prediction of Lymphovascular invasion (LVI) before surgery, a classification model is constructed to predict the LVI status of rectal cancer patients based on the imaging omics method. 212 patients with rectal cancer confirmed by pathological examination were studied, a variety of machine learning algorithms were used to screen the image features extracted by PyRadiomics, and the support vector machine algorithm was used to establish the image model. The logistic regression algorithm with Akaike information criterion as the evaluation index was used to screen the clinical features, and the logistic regression algorithm was used to build the clinical model. The screened image features and clinical features were integrated to build a combined model using the logistic regression algorithm. The results show that the model integrating image features and clinical features has the best prediction ability in both the training set and the test set (training set AUC: 0.954; test set AUC: 0.909). The proposed model can be used to predict the LVI status of rectal cancer patients before surgery, and can be used as an effective clinical tool to guide subsequent individualized treatment.