Objective To investigate the diagnostic performance of radiomics based on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) of MRI for preoperative prediction of extramural vascular invasion (EMVI) in rectal cancer. Methods A total of 168 patients with pathology-confirmed rectal adenocarcinoma were enrolled during January 2010 to June 2023. The patients underwent preoperative rectal MRI scans, and they were randomly divided into training set and validation set at a 7∶3 ratio. Radiomic features from T2WI and DWI were extracted and selected by dimensionality reduction using the maximum relevance minimum redundancy (mRMR) method and the least absolute shrinkage and selection operator (LASSO) regression analysis with ten-fold cross-validation. The radiomic total score (Radscore) for each patient was calculated to make radiomics model. The training set enrolled three clinical features [gender, age and preoperative level of carcinoembryonic antigen (CEA)] and six magnetic resonance imaging features [ADC value, depth of infiltration, tumor length, tumor location, T staging and magnetic resonance imaging-defined extramural vascular invasion (mrEMVI)].The clinical model was established through univariable and multivariable logistic regression analysis based on above clinical and imaging features， and the clinical-radiomics model (combined model) was established with Radscore and independent risk factors from the clinical model. The diagnostic efficacy of each model was assessed using receiver operating characteristic (ROC) curve. The differences in performance among the models were compared using the DeLong test. The Calibration curves were employed to evaluate the consistence between the preoperative predictive results obtained from the nomogram and the postoperative pathological results. Additionally, decision curve analysis (DCA) was applied to evaluate the clinical utility of the three models. Results The area under the curve (AUC) of the ROC curve for the combined model, clinical model, and radiomics model in the training were 0.926, 0.888, 0.756, and were 0.917, 0.896, 0.782 in validation sets, respectively. The DeLong test showed that the diagnostic efficacy of combined model was higher than that of radiomics model in both training and validation sets (P<0.05). The diagnostic efficacy of combined model was better than that of clinical model in the training set (P<0.05). The calibration curve showed the consistency between the preoperative predictive results obtained from the nomogram and the postoperative pathological findings was satisfied. The DCA showed that the risk threshold probabilities between 0.24 and 0.77, the clinical benefit of combined model was higher than those of clinical model and the radiomics model. Conclusions For preoperative prediction of EMVI in rectal cancer,the radiomics model based on T2WI and DWI of MRI has a satisfied diagnostic efficiency, while the clinical-radiomics model (combined model) may further enhance the diagnostic efficiency.