Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

Bijie Bai; Hongda Wang; Yuzhu Li; Kevin de Haan; Francesco Colonnese; Yujie Wan; Jingyi Zuo; Ngan B. Doan; Xiaoran Zhang; Yijie Zhang; Jingxi Li; Xilin Yang; Wenjie Dong; Morgan Angus Darrow; Elham Kamangar; Han Sung Lee; Yair Rivenson; Aydogan Ozcan

doi:10.34133/2022/9786242

BME Frontiers (Jan 2022)

Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

Bijie Bai,
Hongda Wang,
Yuzhu Li,
Kevin de Haan,
Francesco Colonnese,
Yujie Wan,
Jingyi Zuo,
Ngan B. Doan,
Xiaoran Zhang,
Yijie Zhang,
Jingxi Li,
Xilin Yang,
Wenjie Dong,
Morgan Angus Darrow,
Elham Kamangar,
Han Sung Lee,
Yair Rivenson,
Aydogan Ozcan

Affiliations

Bijie Bai: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA; Bioengineering Department, University of California, Los Angeles 90095, USA; California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
Hongda Wang: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA; Bioengineering Department, University of California, Los Angeles 90095, USA; California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
Yuzhu Li: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA; Bioengineering Department, University of California, Los Angeles 90095, USA; California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
Kevin de Haan: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA; Bioengineering Department, University of California, Los Angeles 90095, USA; California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
Francesco Colonnese: Computer Science Department, University of California, Los Angeles, CA, USA
Yujie Wan: Physics and Astronomy Department, University of California, Los Angeles, CA 90095, USA
Jingyi Zuo: Computer Science Department, University of California, Los Angeles, CA, USA
Ngan B. Doan: Translational Pathology Core Laboratory, University of California, Los Angeles, CA 90095, USA
Xiaoran Zhang: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
Yijie Zhang: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA; Bioengineering Department, University of California, Los Angeles 90095, USA; California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
Jingxi Li: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA; Bioengineering Department, University of California, Los Angeles 90095, USA; California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
Xilin Yang: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA; Bioengineering Department, University of California, Los Angeles 90095, USA; California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
Wenjie Dong: Statistics Department, University of California, Los Angeles, CA 90095, USA
Morgan Angus Darrow: Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, CA 95817, USA
Elham Kamangar: Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, CA 95817, USA
Han Sung Lee: Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, CA 95817, USA
Yair Rivenson: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA; Bioengineering Department, University of California, Los Angeles 90095, USA; California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
Aydogan Ozcan: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA; Bioengineering Department, University of California, Los Angeles 90095, USA; California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA; Department of Surgery, University of California, Los Angeles, CA 90095, USA

DOI: https://doi.org/10.34133/2022/9786242
Journal volume & issue: Vol. 2022

Abstract

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The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies, and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

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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