Human Epidermal Growth Factor Receptor

Human Epidermal Growth Factor Receptor 2 (HER2) status is a crucial biomarker for breast cancer prognosis and treatment selection, but current assessment methods are time-consuming and expensive. Research focuses on developing automated, deep learning-based approaches, employing architectures like convolutional neural networks and point transformers, to predict HER2 status directly from readily available hematoxylin and eosin (H&E) stained slides or to improve the accuracy and efficiency of immunohistochemistry (IHC) analysis. These advancements aim to reduce costs, improve diagnostic consistency, and accelerate treatment decisions, particularly benefiting underserved populations with limited access to specialized testing.