Objective: To construct a prediction model of pathologic complete response (pCR) in locally advanced rectal cancer patients who received programmed cell death protein-1 (PD-1) antibody and total neoadjuvant chemoradiotherapy by using radiomics based on MR imaging data and to investigate its predictive value. Methods: A clinical diagnostic test study was carried out. Clinicopathalogical and radiological data of 38 patients with middle-low rectal cancer who received PD-1 antibody combined with total neoadjuvant chemoradiotherapy and underwent TME surgery from January 2019 to September 2021 in our hospital were retrospectively collected. Among 38 patients, 23 were males and 15 were females with a median age of 68 (47-79) years and 13 (34.2%) a chieved pCR. These 38 patients were stratified and randomly divided into the training group (n=26) and test group (n=12) for modeling. All the patients underwent rectal MRI before treatment. The clinical, imaging and radiomics features of all the patients were collected, and the clinical feature model and radiomics model were constructed. The receiver operating characteristic (ROC) curves of each model were drawn, and the constructed model was evaluated through the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Results: There were no significant differences in age, gender, primary location of tumor and postoperative pathology between the two groups (all P>0.05). Forty-one features were extracted from region of interest in each modality, including 9 first-order features, 24 gray level co-occurrence matrix features and 8 shape features. From 38 patients, 41 features were extracted from each imaging modality of baseline and preoperative DWI and T2WI images, totally 164 features. Only 4 features were preserved after correlation analysis between each pair of features and t-test between pCR and non-pCR subjects. After LASSO cross validation, only the first-order skewness of the baseline DWI image before treatment and the volume in the baseline T2WI image before treatment were retained. The area under the curve, sensitivity, specificity, positive and negative predictive values of the prediction model established by applying these two features in the training group and the test group were 0.856 and 0.844, 77.8% and 100.0%, 88.2% and 75.0%, 77.8% and 66.7%, 88.2% and 100.0%, respectively. The decision curve analysis of the radiomics model showed that the strategy of this model in predicting pCR was better than that in treating all the patients as pCR and that in treating all the patients as non-pCR. Conclusion: The pCR prediction model for rectal cancer patients receiving PD-1 antibody combined with total neoadjuvant radiochemotherapy based on MRI radiomics has the potential to be used in clinical screening or rectal cancer patients who can be spared from radical surgery.