Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling

作     者：Selcuk, Sahan Yoruc Yang, Xilin Bai, Bijie Zhang, Yijie Li, Yuzhu Aydin, Musa Unal, Aras Firat Gomatam, Aditya Guo, Zhen Angus, Darrow Morgan Kolodney, Goren Atlan, Karine Haran, Tal Keidar Pillar, Nir Ozcan, Aydogan 

作者机构：Electrical and Computer Engineering Department University of California Los AngelesCA United States Bioengineering Department University of California Los AngelesCA United States California NanoSystems Institute University of California Los AngelesCA United States Department of Pathology and Laboratory Medicine University of California at Davis SacramentoCA United States Bnai-Zion Medical Center Haifa Israel Hadassah Hebrew University Medical Center Jerusalem Israel David Geffen School of Medicine University of California Los AngelesCA United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Tissue 

摘      要：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. Copyright © 2024, The Authors. All rights reserved.