Insights into AI advances in immunohistochemistry for effective breast cancer treatment: a literature review of ER, PR, and HER2 scoring.

Abstract

Breast cancer is a significant health challenge, with accurate and timely diagnosis being critical to effective treatment. Immunohistochemistry (IHC) staining is a widely used technique for the evaluation of breast cancer markers, but manual scoring is time-consuming and can be subject to variability. With the rise of Artificial Intelligence (AI), there is an increasing interest in using machine learning and deep learning approaches to automate the scoring of ER, PR, and HER2 biomarkers in IHC-stained images for effective treatment. This narrative literature review focuses on AI-based techniques for the automated scoring of breast cancer markers in IHC-stained images, specifically Allred, Histochemical (H-Score) and HER2 scoring. We aim to identify the current state-of-the-art approaches, challenges, and potential future research prospects for this area of study. By conducting a comprehensive review of the existing literature, we aim to contribute to the ultimate goal of improving the accuracy and efficiency of breast cancer diagnosis and treatment.