Abstract:Background and Aims HER2-positive breast cancer is characterized by strong invasiveness and marked heterogeneity. Although neoadjuvant chemotherapy (NAC) combined with anti-HER2 targeted therapy significantly improves the pathological complete response (pCR) rate, therapeutic responses vary substantially among patients. Therefore, developing a noninvasive pretreatment predictive model is of great importance for individualized treatment optimization. This study aimed to evaluate the clinical value of machine learning models based on ultrasound radiomics and clinicopathological features in predicting pCR in HER2-positive breast cancer after NAC combined with anti-HER2 targeted therapy.Methods A total of 353 patients with pathologically confirmed HER2-positive breast cancer who received NAC combined with anti-HER2 targeted therapy between October 2019 and October 2024 were retrospectively enrolled from Hunan Cancer Hospital and the Third Xiangya Hospital of Central South University. Among them, 275 patients from Hunan Cancer Hospital were assigned to the internal dataset, while 78 patients from the Third Xiangya Hospital were used as the external validation dataset. Ultrasound radiomics features were extracted from pretreatment ultrasound images and combined with clinicopathological features. After correlation analysis and least absolute shrinkage and selection operator (LASSO) regression for feature selection, Extreme Gradient Boosting (XGBoost), Random Forest, and Na?ve Bayes algorithms were applied to construct clinical models, radiomics models, and combined models. Receiver operating characteristic curves were used to evaluate predictive performance, decision curve analysis (DCA) was performed to assess clinical utility, and Shapley Additive Explanation (SHAP) analysis was used to interpret feature importance in the optimal model.Results Twelve clinicopathologic features and seven ultrasound radiomics features were finally selected for model construction. Among the three algorithms, the XGBoost model demonstrated the best predictive performance. Compared with the clinical model or radiomics model alone, the combined model achieved superior predictive efficacy. The AUCs of the XGBoost combined model for predicting pCR were 0.964 (95% CI=0.893-0.965), 0.857 (95% CI=0.822-0.960), and 0.771 (95% CI=0.765-0.946) in the training, internal validation, and external validation cohorts, respectively. DCA demonstrated favorable net clinical benefits across a wide range of threshold probabilities. SHAP analysis revealed that HER2 expression status, targeted therapy strategy, estrogen receptor status, and several texture features contributed substantially to model prediction.Conclusion The XGBoost machine learning model integrating ultrasound radiomics and clinicopathological features can effectively predict pCR after NAC combined with anti-HER2 targeted therapy in HER2-positive breast cancer, showing promising clinical utility for individualized therapeutic decision-making.