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

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

doi:10.34133/bmef.0048

BME Frontiers (Jan 2024)

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

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

Affiliations

Sahan Yoruc Selcuk: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Xilin Yang: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Bijie Bai: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Yijie Zhang: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Yuzhu Li: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Musa Aydin: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Aras Firat Unal: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Aditya Gomatam: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Zhen Guo: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Darrow Morgan Angus: Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, CA, USA.
Goren Kolodney: Bnai-Zion Medical Center, Haifa, Israel.
Karine Atlan: Hadassah Hebrew University Medical Center, Jerusalem, Israel.
Tal Keidar Haran: Hadassah Hebrew University Medical Center, Jerusalem, Israel.
Nir Pillar: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Aydogan Ozcan: Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.

DOI: https://doi.org/10.34133/bmef.0048
Journal volume & issue: Vol. 5

Abstract

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Objective and Impact Statement: 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. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of 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. Methods: Our deep learning-based method 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. Results: This approach 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. Conclusion: This automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might substantially impact cancer treatment planning.

Published in BME Frontiers

ISSN: 2765-8031 (Online)
Publisher: American Association for the Advancement of Science (AAAS)
Country of publisher: United States
LCC subjects: Medicine: Medicine (General): Medical technology; Technology: Chemical technology: Biotechnology
Website: https://spj.science.org/journal/bmef

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