Deep learning-based transformation of the H E stain into special stains improves kidney disease diagnosis
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Pathology is practiced by visual inspection of histochemically stained slides. Most commonly, the hematoxylin and eosin (H E) stain is used in the diagnostic workflow and it is the gold standard for cancer diagnosis. However, in many cases, especially for non-neoplastic diseases, additional "special stains" are used to provide different levels of contrast and color to tissue components and allow pathologists to get a clearer diagnostic picture. In this study, we demonstrate the utility of supervised learning-based computational stain transformation from H E to different special stains (Masson's Trichrome, periodic acid-Schiff and Jones silver stain) using tissue sections from kidney needle core biopsies. Based on evaluation by three renal pathologists, followed by adjudication by a fourth renal pathologist, we show that the generation of virtual special stains from existing H E images improves the diagnosis in several non-neoplastic kidney diseases, sampled from 16 unique subjects. Adjudication of N=48 diagnoses from the three pathologists revealed that the virtually generated special stains yielded 22 improvements (45.8 concordances (47.9 of H E stained tissue only. As the virtual transformation of H E images into special stains can be achieved in less than 1 min per patient core specimen slide, this stain-to-stain transformation framework can improve the quality of the preliminary diagnosis when additional special stains are needed, along with significant savings in time and cost, reducing the burden on healthcare system and patients.
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