Objective The objective of this study is to evaluate the diagnostic value of ultrasonographic models and clinical parameters in patients with stage 1 triple-negative breast cancer and fibroadenoma. Methods A retrospective analysis was conducted on clinical and ultrasound imaging data from 133 patients with stage 1 triple-negative breast and 207 patients with fibronenoma who met the inclusion criteria at our hospital between February 2020 and November 2023. The dataset was randomly divided into a training set （n=238） and a validation set （n=102） in a 7：3 ratio. The radiomics features were obtained using the radiomics extension package of the 3D-slicer software，and the image omics model was established using LASSO and logistic regression. Through univariate analysis，combined with patient age and BI-RADS classification，a clinical model was established for the differential diagnosis of triple-negative breast cancer and fibroadenoma. The area under the receiver operating characteristic curve （AUC） was calculated to evaluate the performance of each model.Results Ultimately, 851 ultrasonic image omics features were extracted from each patient''s ultrasound images, representing the final cohort of stage 1 TNBC and fibrosarcoma patients included in the study. Following the normalization of the data set, 12 features were ultimately obtained through Chi-square test, LASSO regression, and 10-fold cross-validation. The area under the receiver operating characteristic curve for the ultrasonic image omics model constructed by this method is 0.954 and 0.881 for the training set and validation set, respectively. The area under the receiver operating characteristic curve (AUC) for the clinical model in the training set and validation set was 0.837 and 0.815, respectively. The AUC for the combined model in the training set and validation set was 0.983 and 0.942, respectively. Conclusion The combination of an ultrasound-based image omics model with clinical information represents a promising approach for the non-invasive quantitative analysis of stage 1 triple negative breast cancer and fibladenoma. This approach has demonstrated efficacy in the differentiation of these two entities.