Hermsen Meyke, Ciompi Francesco, Adefidipe Adeyemi, Denic Aleksandar, Dendooven Amélie, Smith Byron H, van Midden Dominique, Bräsen Jan Hinrich, Kers Jesper, Stegall Mark D, Bándi Péter, Nguyen Tri, Swiderska-Chadaj Zaneta, Smeets Bart, Hilbrands Luuk B, van der Laak Jeroen A W M
Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.
Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands.
Am J Pathol. 2022 Oct;192(10):1418-1432. doi: 10.1016/j.ajpath.2022.06.009. Epub 2022 Jul 16.
In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3 cell density within scarred regions and higher CD3 cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.
在肾移植活检中，炎症和慢性改变都是预测移植物长期存活的重要特征。对这些特征进行定量评分对于移植诊断和肾脏研究至关重要。然而，视觉评分的可重复性差且劳动强度大。本研究的目的是探讨卷积神经网络（CNN）对肾移植活检中炎症和慢性特征进行量化的潜力。将一个结构分割CNN和一个淋巴细胞检测CNN应用于125对过碘酸希夫染色和CD3染色玻片的全切片图像。CNN的结果用于量化健康和硬化的肾小球、间质纤维化、肾小管萎缩，以及非萎缩性和萎缩性肾小管内及间质纤维化区域的炎症。计算得到的组织特征与五位病理学家（A.A.、A.Dend.、J.H.B.、J.K.和T.N.）的班夫病变评分高度相关。对一个小子集的分析显示，瘢痕区域内较高的CD3细胞密度与中等程度的相关性，以及萎缩性肾小管内较高的CD3细胞计数与估计肾小球滤过率的长期变化相关。所提出的CNN是有效的工具，能够以可重复的方式提供关于瘢痕化和未瘢痕化肾实质内肾小球数量、纤维化组织和炎症的客观定量信息。CNN有潜力改善肾移植诊断，并作为一种为大规模临床研究生成替代终点的新方法造福于该领域。
