Chyrmang Genevieve, Bora Kangkana, Das Anup Kr, Ahmed Gazi N, Kakoti Lopamudra
Department of Computer Science and Information Technology, Cotton University, Guwahati, Assam, India.
Arya Wellness Centre, Guwahati, Assam, India.
Curr Med Res Opin. 2025 Jan;41(1):115-134. doi: 10.1080/03007995.2024.2445142. Epub 2025 Jan 6.
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.
乳腺癌是一项重大的健康挑战，准确及时的诊断对于有效治疗至关重要。免疫组织化学（IHC）染色是评估乳腺癌标志物的一种广泛使用的技术，但手动评分耗时且可能存在变异性。随着人工智能（AI）的兴起，人们越来越有兴趣使用机器学习和深度学习方法来自动对免疫组化染色图像中的雌激素受体（ER）、孕激素受体（PR）和人表皮生长因子受体2（HER2）生物标志物进行评分，以实现有效治疗。这篇叙述性文献综述聚焦于基于人工智能的技术，用于对免疫组化染色图像中的乳腺癌标志物进行自动评分，特别是艾尔雷德（Allred）评分、组织化学（H评分）和HER2评分。我们旨在确定该研究领域当前的先进方法、挑战以及潜在的未来研究前景。通过对现有文献进行全面综述，我们旨在为提高乳腺癌诊断和治疗的准确性及效率这一最终目标做出贡献。
