Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin–eosin-stained slides

作者：Jun Du, Jun Shi, Dongdong Sun, Yifei Wang, Guanfeng Liu, Jingru Chen, Wei Wang, Wenchao Zhou, Yushan Zheng, Haibo Wu · 发表于：Breast Cancer Research · 年份：2025 · DOI：10.1186/s13058-025-01998-8 · 被引用次数：7 · 研究领域：HER2/EGFR in Cancer Research、AI in cancer detection、Cell Image Analysis Techniques

BACKGROUND: Treatment with HER2-targeted therapies is recommended for HER2-positive breast cancer patients with HER2 gene amplification or protein overexpression. Interestingly, recent clinical trials of novel HER2-targeted therapies demonstrated promising efficacy in HER2-low breast cancers, raising the prospect of including a HER2-low category (immunohistochemistry, IHC) score of 1 + or 2 + with non-amplified in-situ hybridization for HER2-targeted treatments, which necessitated the accurate detection and evaluation of HER2 expression in tumors. Traditionally, HER2 protein levels are routinely assessed by IHC in clinical practice, which not only requires significant time consumption and financial investment but is also technically challenging for many basic hospitals in developing countries. Therefore, directly predicting HER2 expression by hematoxylin-eosin (HE) staining should be of significant clinical values, and machine learning may be a potent technology to achieve this goal. METHODS: In this study, we developed an artificial intelligence (AI) classification model using whole slide image of HE-stained slides to automatically assess HER2 status. RESULTS: A publicly available TCGA-BRCA dataset and an in-house USTC-BC dataset were applied to evaluate our AI model and the state-of-the-art method SlideGraph + in terms of accuracy (ACC), the area under the receiver operating characteristic curve (AUC), and F1 score. Overall, our AI model achieved the superior performance in...