Objective：To develop and validate a radiomics nomogram based on intratumoral and peritumoral multiparametric magnetic resonance imaging（MRI）for preoperative prediction of N2 - 3 stage axillary lymph node（ALN）in invasive breast cancer. Methods：We retrospectively analyzed 320 invasive breast cancer patients（224 in the training set and 96 in the validation set）who underwent preoperative standardized breast MRI from January 2018 to December 2019. Based on the number of axillary metastatic lymph nodes in the pathological reports，patients were divided into N0-1 group（fewer than 4 metastatic nodes）and N2-3 group（4 or more metastatic nodes）. Radiomics features associated with ALN stage were selected from three regions of interest（ROI）across five MRI sequences：intratumor，peritumor，and intratumor + peritumor. Three radiomics models（intratumoral model，peritumoral model， and intratumoral + peritumoral model）were constructed using a random forest（RF）classifier，and radiomics scores for each model were calculated. Univariate and multivariate logistic regression were performed to identify clinical parameters and then constructed a clinical model. Features from the optimal radiomics model and clinical model were integrated to build a combined model and visualized as a nomogram. The predictive performance and clinical utility of each model were evaluated and compared using receiver operating characteristic（ROC）curves，calibration curves，and decision curve analysis（DCA）. Results：The intratumoral + peritumoral model performed best among the three radiomics models，with an area under the curve（AUC）of 0.911 and 0.858 in the training and validation sets，respectively. The nomogram integrating the intratumoral + peritumoral radiomics features and clinical features （peritumoral edema and lesion enhancement pattern）had the best predictive efficacy of N2-3 stage ALN among all models，with an AUC of 0.923 and 0.892 in the training and validation datasets，respectively. Calibration curves showed that the predicted values of the nomogram were in good agreement with the actual observed values. DCA demonstrated that the nomogram had high clinical utility. Conclusion：A nomogram constructed by multiregion multiparametric MRI radiomics features and clinical features has high value for personalized prediction of N2-3 stage ALN in invasive breast cancer.