AUTHOR=Zhang Huiting , Lang Manlin , Shen Huiming , Li Hang , Yang Ning , Chen Bo , Chen Yixu , Ding Hong , Yang Weiping , Ji Xiaohui , Zhou Ping , Cui Ligang , Wang Jiandong , Xu Wentong , Ye Xiuqin , Liu Zhixing , Yang Yu , Wei Tianci , Wang Hui , Yan Yuanyuan , Wu Changjun , Wu Yiyun , Shi Jingwen , Wang Yaxi , Fang Xiuxia , Li Ran , Liang Ping , Yu Jie 

TITLE=Machine learning-based fusion model for predicting HER2 expression in breast cancer by Sonazoid-enhanced ultrasound: a multicenter study

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1585823

DOI=10.3389/fmed.2025.1585823

ISSN=2296-858X

ABSTRACT=PurposeTo predict human epidermal growth factor receptor 2 (HER2) expression in breast cancer (BC) using Sonazoid-enhanced ultrasound in a machine learning-based model.Materials and methodsBetween August 2020 and February 2021, patients with breast cancer who underwent surgical treatment without neoadjuvant chemotherapy were prospectively enrolled from 17 hospitals in China. HER2 expression status was assessed by immunohistochemistry or fluorescence in situ hybridization (FISH). The training set contained data from 11 hospitals and the validation set contained 6 hospitals. Clinical features, B-mode ultrasound, contrast-enhanced ultrasound (CEUS), and time-intensity curve were selected by the Least Absolute Shrinkage and Selection Operator. Based on the selected features, six prediction models were established to predict HER2 3 + and 2 +/1 + expression: logistic regression (LR), support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), XGB combined with LR, and fusion model.ResultsA total of 140 patients with breast cancer were enrolled in this study. Seven features related to HER2 3 + and six features related to HER2 2+/1 + were selected to establish prediction models. Among the six models, LR, SVM, and XGB showed the best prediction performance for both HER2 3 + and HER2 2+/1 + cases. These three models were then combined into a fusion model. In the validation, the fusion model achieved the highest value of area under the receiver operating characteristic curve as 0.869 (95%CI: 0.715–0.958) for predicting HER2 3 + and 0.747 (95%CI: 0.548–0.891) for predicting HER2 2+/1 + cases. The model could correctly upgrade HER2 2 + cases to HER2 3 + cases, consistent with the FISH test results.ConclusionSonazoid-enhanced ultrasound can provide effective guidance for targeted therapy of breast cancer by predicting HER2 expression using machine learning approaches.