Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis

Key Points Question Can deep learning systems perform hematoxylin and eosin (H&E) staining and destaining and are the virtual core biopsy samples generated by them as valid and interpretable as their real-life unstained and H&E dye–stained counterparts? Findings In this cross-sectional study, deep learning models were trained using nonstained prostate core biopsy images to generate computationally H&E stained images, and core biopsy images were extracted from each whole slide, consisting of approximately 87 000 registered patch pairs of 1024 × 1024 × 3 pixels each. Comprehensive analyses of virtually stained images vs H&E dye–stained images confirmed successful computational staining. Meaning The findings of this study suggest that whole slide nonstained microscopic images of prostate core biopsy, instead of tissue samples, could be integrated with deep learning algorithms to perform computational H&E staining and destaining for rapid and accurate tumor diagnosis.