Automated annotations of epithelial cells and stroma in hematoxylin–eosin‐stained whole‐slide images using cytokeratin re‐staining. (30th October 2021)

• Record Type: Journal Article
• Authors: Brázdil, Tomáš; Gallo, Matej; Nenutil, Rudolf; Kubanda, Andrej; Toufar, Martin; Holub, Petr
• Is Part Of: Journal of pathology. Volume 8:Number 2(2022)
• Page Start: 129
• Page End: 142
• Publication Date: 2021-10-30
• Subjects: U‐Net -- artificial intelligence -- digital pathology -- H&E -- immunohistochemistry -- deep learning -- tissue registration; Pathology -- Periodicals; Diagnosis, Laboratory -- Periodicals; 616.07
• DOI: 10.1002/cjp2.249
• Languages: English
• ISSNs: 2056-4538

Abstract

Abstract: The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape, together with stromal cells, extracellular matrix, and blood vessels. Distinguishing stroma from epithelium is a critical component of artificial intelligence (AI) methods developed to detect and analyze carcinomas. In this paper, we propose a novel automated workflow that enables large‐scale guidance of AI methods to identify the epithelial component. The workflow is based on re‐staining existing hematoxylin and eosin (H&E) formalin‐fixed paraffin‐embedded sections by immunohistochemistry for cytokeratins, cytoskeletal components specific to epithelial cells. Compared to existing methods, clinically available H&E sections are reused and no additional material, such as consecutive slides, is needed. We developed a simple and reliable method for automatic alignment to generate masks denoting cytokeratin‐rich regions, using cell nuclei positions that are visible in both the original and the re‐stained slide. The registration method has been compared to state‐of‐the‐art methods for alignment of consecutive slides and shows that, despite being simpler, it provides similar accuracy and is more robust. We also demonstrate how the automatically generated masks can be used to train modern AI image segmentation based on U‐Net, resulting in reliable detection of epithelial regions in previously unseen H&E slides. Through training on real‐world material available in clinical laboratories, this approach therefore has widespread applications toward achieving AI‐assisted tumor assessment directly from scanned H&E sections. In addition, the re‐staining method will facilitate additional automated quantitative studies of tumor cell and stromal cell phenotypes.