Convolutional neural networks for the evaluation of chronic and inflammatory lesions in kidney transplant biopsies

M. Hermsen, F. Ciompi, A. Adefidipe, A. Denic, A. Dendooven, B. Smith, D. van Midden, J. Bräsen, J. Kers, M. Stegall, P. Bándi, T. Nguyen, Z. Swiderska-Chadaj, B. Smeets, L. Hilbrands and J. van der Laak

American Journal of Pathology 2022.


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 PAS-, and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation both within non-atrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlations with Banff lesion scores of five pathologists. Analyses on a small subset showed a moderate correlation towards 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 fashion. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate endpoints for large-scale clinical studies.