Faculty of Informatics, Masaryk University, Brno, Czech Republic.
Department of Pathology, Masaryk Memorial Cancer Institute, Brno, Czech Republic.
J Pathol Clin Res. 2022 Mar;8(2):129-142. doi: 10.1002/cjp2.249. Epub 2021 Oct 30.
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.
上皮来源的实体肿瘤（癌）的诊断是临床组织病理学工作量的主要部分。癌由排列成或多或少具有不同大小和形状的有凝聚力的簇的恶性上皮细胞组成，以及间质细胞、细胞外基质和血管。区分基质和上皮是为了识别癌而开发的人工智能（AI）方法的关键组成部分。在本文中，我们提出了一种新颖的自动化工作流程，该流程可大规模指导 AI 方法来识别上皮成分。该工作流程基于通过免疫组织化学对现有的苏木精和伊红（H&E）福尔马林固定石蜡包埋切片进行重新染色，以检测细胞角蛋白和上皮细胞特有的细胞骨架成分。与现有方法相比，可重复使用临床可用的 H&E 切片，且无需额外的材料，例如连续切片。我们开发了一种简单可靠的自动对齐方法，使用在原始和重新染色的载玻片上均可见的细胞核位置来生成表示细胞角蛋白丰富区域的蒙版。该注册方法已与用于连续切片对齐的最新方法进行了比较，结果表明，尽管它更简单，但它提供了相似的准确性和更高的鲁棒性。我们还展示了如何使用自动生成的蒙版来训练基于 U-Net 的现代 AI 图像分割，从而可靠地检测以前看不见的 H&E 切片中的上皮区域。通过在临床实验室中可用的真实材料上进行训练，因此，该方法可广泛应用于直接从扫描的 H&E 切片实现 AI 辅助肿瘤评估。此外，重新染色方法将促进对肿瘤细胞和基质细胞表型的进一步自动定量研究。
