AI-Based Staining Technique as Accurate as Traditional Histopathology in Assessing Breast Cancer Biomarker

The research team at UCLA (Los Angeles, CA, USA) captured the autofluorescence information of the unstained breast tissue, which is naturally emitted by biological structures when they absorb light. They further trained a deep neural network that rapidly transforms these stain-free autofluorescence images into virtual histological images, revealing the accurate color and contrast as if the tissue sections were chemically stained for HER2. This computational staining process takes only a few minutes per sample and does not need expensive facilities or toxic chemicals. Using only a computer, the HER2 staining could be accomplished much faster and cost-effectively, accelerating breast cancer assessments and treatment.

Board-certified pathologists blindly validated this AI-based virtual HER2 staining technique in terms of both its diagnostic value and stain quality. The pathologists confirmed that the deep learning-generated images provide the equivalent diagnostic accuracy for HER2 assessment and have a staining quality comparable to the standard images chemically stained in the laboratory. This deep learning-powered virtual HER2 staining approach eliminates the need for costly, laborious, and time-consuming HER2 staining procedures performed by histology experts and could be extended to staining of other cancer-related biomarkers to accelerate the traditional histopathology and diagnostic workflow in clinical settings.

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