Graph neural networks in the nephropathological diagnosis of antibody-mediated rejection (AMR)

Abstract

Background and Aims

Antibody-mediated rejection (AMR) remains a significant cause of late allograft failure. However, considerable variability exists in its diagnosis, even among experts. Weakly supervised machine learning applied to renal biopsy whole slide images (WSIs) could offer cost-efficient and accurate diagnostics with perfect reproducibility.

In this study, we build on our previous work developing diagnostic models for AMR using a multi-institutional dataset including adversarial samples such as accommodation, transmitted, recurrent, and de novo diseases.

Method

A dataset of 1,183 periodic acid-Schiff WSIs from 348 patients from four different institutions was automatically segmented into tissue compartment crops. Graph neural networks (GNNs) were employed to classify AMR and non-AMR (including adversarial samples like accommodation, transmitted and de novo glomerulopathy). The WSIs were represented as fully connected graphs, with glomerular crops as nodes, capturing global spatial relationships. Feature vectors for individual glomerular crops were computed using both supervised (Swin Transformer) and self-supervised (MAE and SimCLR) architectures. Classification was performed using Graph-Transformer and three novel models: SimpleGCN, DenseGCN, and SimpleGAT. These WSI-level classifiers were compared to state-of-the-art patch-level classification methods (Swin and ConvNeXt). Performance was determined in 5-fold internal cross-validation experiments.

Results

The GNN-based methods outperformed baseline patch-level classification models. The best- performing model, SimpleGCN with Swin-extracted features, achieved an accuracy of 71.00% and an AUC of 0.7858, significantly better than the Swin model (accuracy of 65.66% and an AUC of 0.7265).

Conclusion

This study shows the potential of graph-based representations to model contextual information in nephropathology images. Our approach permits easy upscaling of training cohorts for cost-efficient and even more accurate diagnostic support systems. To this end and for further validation we are actively seeking collaborators.

GB and JUB contributed equally.