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4 October 2022 Virtual immunohistochemical (IHC) staining of unlabeled tissue using deep learning (Conference Presentation)
Bijie Bai, Hongda Wang, Yuzhu Li, Kevin De Haan, Francesco Colonnese, Yujie Wan, Jingyi Zuo, Ngan Doan, Xiaoran Zhang, Yijie Zhang, Jingxi Li, Wenjie Dong, Morgan A. Darrow, Elham Kamangar, Han Sung Lee, Yair Rivenson, Aydogan Ozcan
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Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040O (2022) https://doi.org/10.1117/12.2632652
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States
Abstract
Immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) is routinely performed on breast cancer cases to guide immunotherapies and help predict the prognosis of breast tumors. We present a label-free virtual HER2 staining method enabled by deep learning as an alternative digital staining method. Our blinded, quantitative analysis based on three board-certified breast pathologists revealed that evaluating HER2 scores based on virtually-stained HER2 whole slide images (WSIs) is as accurate as standard IHC-stained WSIs. This virtual HER2 staining can be extended to other IHC biomarkers to significantly improve disease diagnostics and prognostics.
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Bijie Bai, Hongda Wang, Yuzhu Li, Kevin De Haan, Francesco Colonnese, Yujie Wan, Jingyi Zuo, Ngan Doan, Xiaoran Zhang, Yijie Zhang, Jingxi Li, Wenjie Dong, Morgan A. Darrow, Elham Kamangar, Han Sung Lee, Yair Rivenson, and Aydogan Ozcan "Virtual immunohistochemical (IHC) staining of unlabeled tissue using deep learning (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040O (4 October 2022); https://doi.org/10.1117/12.2632652
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KEYWORDS
Tissues
Breast
Breast cancer
Diagnostics
Neural networks
Quantitative analysis
Receptors
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