Objective To explore the application value of intratumoral and peritumoral ultrasomics in predicting the expression level of Ki-67 in breast invasive ductal carcinoma.Methods A retrospective study was conducted on 300 patients who underwent breast surgery and were pathologically confirmed to be breast invasive ductal carcinoma,including 169 cases in low expression group(Ki-67<30%)and 131 cases in high expression group(Ki-67≥30%).The largest cross-section of the lesion on the two-dimensional ultrasound images was selected to delineate the region of interest which was automatically expanded outward by 5 mm to obtain the peritumoral area,and the intratumoral and peritumoral ultrasomics features were extracted.The patients were randomly divided into training group(n=210)and validation group(n=90)in a ratio of 7:3.All features were screened through Z-score standardization,t test,Pearson correlation coefficient and least absolute shrinkage and selection operator algorithm to obtain the optimal feature combination.Random forest(RF)model was used to classify the expression level of Ki-67 in breast invasive ductal carcinoma,and 3 models(intratumoral,peritumoral and combined models)were established,whose diagnostic efficacies in predicting the expression level of Ki-67 in breast invasive ductal carcinoma were evaluated using receiver operating characteristic curve.Results The RF model constructed based on the combination of intratumoral and peritumoral ultrasomics features performed better in predicting the Ki-67 expression status in breast cancer,with the AUC values of 0.899(95%CI:0.860-0.939)in training group and 0.832(95%CI:0.746-0.917)in validation group,demonstrating its superior diagnostic efficacy over the intratumoral and peritumoral radiomics alone(P<0.05).Conclusion A RF model constructed based on intratumoral combined with peritumoral ultrasomics features which has good diagnostic performance in predicting the expression level of Ki-67 in breast invasive ducts has the potential to serve as a non-invasive tool,providing useful information for personalized treatment strategy of breast invasive ductal carcinoma.