Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Accurate assessment of immunohistochemically (IHC) stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in IHC-stained BC tissue images. Our approach analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. This method addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Our automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might significantly impact cancer treatment planning.
@article{arxiv.2404.00837,
title = {Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling},
author = {Sahan Yoruc Selcuk and Xilin Yang and Bijie Bai and Yijie Zhang and Yuzhu Li and Musa Aydin and Aras Firat Unal and Aditya Gomatam and Zhen Guo and Darrow Morgan Angus and Goren Kolodney and Karine Atlan and Tal Keidar Haran and Nir Pillar and Aydogan Ozcan},
journal= {arXiv preprint arXiv:2404.00837},
year = {2024}
}