Prediction of neoadjuvant chemotherapy efficacy in patients with HER2-low breast cancer based on ultrasound radiomics.
Authors
Affiliations (7)
Affiliations (7)
- School of Health Management, China Medical University, Shenyang, Liaoning, 110122, China.
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, China.
- Department of Breast Surgery, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, Liaoning Province, 110001, P. R. China.
- Department of Breast Surgery, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, Liaoning Province, 110001, P. R. China. [email protected].
- School of Health Management, China Medical University, Shenyang, Liaoning, 110122, China. [email protected].
- China Medical University, No.77 Puhe Road, Shenbei New District, Shenyang, Liaoning, 110122, China. [email protected].
- Department of Breast Surgery, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, Liaoning Province, 110001, P. R. China. [email protected].
Abstract
Neoadjuvant chemotherapy (NAC) is a crucial therapeutic approach for treating breast cancer, yet accurately predicting treatment response remains a significant clinical challenge. Conventional ultrasound plays a vital role in assessing tumor morphology but lacks the ability to quantitatively capture intratumoral heterogeneity. Ultrasound radiomics, which extracts high-throughput quantitative imaging features, offers a novel approach to enhance NAC response prediction. This study aims to evaluate the predictive efficacy of ultrasound radiomics models based on pre-treatment, post-treatment, and combined imaging features for assessing the NAC response in patients with HER2-low breast cancer. This retrospective multicenter study included 359 patients with HER2-low breast cancer who underwent NAC between January 1, 2016, and December 31, 2020. A total of 488 radiomic features were extracted from pre- and post-treatment ultrasound images. Feature selection was conducted in two stages: first, Pearson correlation analysis (threshold: 0.65) was applied to remove highly correlated features and reduce redundancy; then, Recursive Feature Elimination with Cross-Validation (RFECV) was employed to identify the optimal feature subset for model construction. The dataset was divided into a training set (244 patients) and an external validation set (115 patients from independent centers). Model performance was assessed via the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. Three models were initially developed: (1) a pre-treatment model (AUC = 0.716), (2) a post-treatment model (AUC = 0.772), and (3) a combined pre- and post-treatment model (AUC = 0.762).To enhance feature selection, Recursive Feature Elimination with Cross-Validation was applied, resulting in optimized models with reduced feature sets: (1) the pre-treatment model (AUC = 0.746), (2) the post-treatment model (AUC = 0.712), and (3) the combined model (AUC = 0.759). Ultrasound radiomics is a non-invasive and promising approach for predicting response to neoadjuvant chemotherapy in HER2-low breast cancer. The pre-treatment model yielded reliable performance after feature selection. While the combined model did not substantially enhance predictive accuracy, its stable performance suggests that longitudinal ultrasound imaging may help capture treatment-induced phenotypic changes. These findings offer preliminary support for individualized therapeutic decision-making.