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. 2025 May 21:12:1585823.
doi: 10.3389/fmed.2025.1585823. eCollection 2025.

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

Huiting Zhang #  1 , Manlin Lang #  1 , Huiming Shen  2 , Hang Li  3 , Ning Yang  4 , Bo Chen  5 , Yixu Chen  6 , Hong Ding  7 , Weiping Yang  8 , Xiaohui Ji  9 , Ping Zhou  10 , Ligang Cui  11 , Jiandong Wang  12 , Wentong Xu  12 , Xiuqin Ye  13 , Zhixing Liu  14 , Yu Yang  15 , Tianci Wei  16 , Hui Wang  17 , Yuanyuan Yan  18 , Changjun Wu  19 , Yiyun Wu  20 , Jingwen Shi  21 , Yaxi Wang  22 , Xiuxia Fang  22 , Ran Li  23 , Ping Liang  1 , Jie Yu  1
Affiliations

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

Huiting Zhang et al. Front Med (Lausanne). .

Abstract

Purpose: To 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 methods: Between 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.

Results: A 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.

Conclusion: Sonazoid-enhanced ultrasound can provide effective guidance for targeted therapy of breast cancer by predicting HER2 expression using machine learning approaches.

Keywords: Sonazoid; breast cancer; human epidermal growth factor receptor 2; machine learning; ultrasound.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of study design. HER2: human epidermal growth factor receptor-2; IHC: immunohistochemistry; TIC: time intensity curve; CEUS: contrast-enhanced ultrasound. LR: logistic regression; SVM: support vector machine; RF: random forest; XGB: XGBoost.
Figure 2
Figure 2
CEUS and B-mode ultrasound images of a patient with HER2-positive breast cancer.
Figure 3
Figure 3
Feature selection in B-mode, CEUS, and TIC of the CEUS group by LASSO regression in 140 patients with breast cancer. (a,b) Selection of B-mode ultrasound and CEUS features. (c,d) Selection of TIC parameters.
Figure 4
Figure 4
Hard voting progression of the decision-level fusion model.
Figure 5
Figure 5
ROCs of the classifiers in predicting HER2-positive breast cancer based on B-mode ultrasound, CEUS, and TIC in the (a) training and (b) validation sets.
Figure 6
Figure 6
The ROCs of the six prediction models in predicting HER2 low expression patients in the (a) training and (b) validation sets.
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