We present a virtual immunohistochemical (IHC) staining method based on label-free autofluorescence imaging and deep learning. Using a trained neural network, we transform multi-band autofluorescence images of unstained tissue sections to their bright-field equivalent HER2 images, matching the microscopic images captured after the standard IHC staining of the same tissue sections. Three pathologists’ blind evaluations of HER2 scores based on virtually stained and IHC-stained whole slide images revealed the statistically equivalent diagnostic values of the two methods. This virtual HER2 staining method provides a rapid, accurate, and low-cost alternative to the standard IHC staining methods and allows tissue preservation.
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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