Available online 8 October 2025

GNNs improve AMR diagnosis accuracy by up to 21.31 % over patch-wise models.

Multi-compartment graphs yield 89.53 % biopsy-level accuracy on AMR.

Fully automated pipeline enables large-scale WSI analysis with minimal expert input.

Antibody-mediated rejection (AMR) is a leading cause of kidney transplant failure, requiring accurate histopathological assessment for diagnosis. This study evaluates graph-based deep learning models for AMR classification using periodic acid–Schiff (PAS)-stained whole slide images (WSIs), with the aim of improving diagnostic accuracy and reproducibility. A multi-institutional dataset of 1193 WSIs from 348 patients was used, where glomeruli, arteries, and cortical tubulointerstitial regions were segmented via deep learning and represented as nodes in graph-structured data. Feature extraction was performed using both supervised and self-supervised methods, and classification was conducted with four graph neural network (GNN) architectures: Graph-Transformer, SimpleGCN, DenseGCN and SimpleGAT. Patch-wise convolutional and transformer-based classifiers served as baselines. All models were evaluated at both the WSI and biopsy levels using stratified five-fold cross-validation. GNN-based models consistently outperformed patch-wise baselines, with the best glomeruli-only GNN achieving a 5.34 % improvement in WSI-level accuracy (71.00 %) over the strongest baseline. Incorporating additional compartments (arteries and cortex) further improved accuracy to 86.97 % at the WSI level and 89.53 % at the biopsy level, with statistically significant gains confirming the additive value of multi-compartment modeling. Performance varied across feature extractors and graph configurations, underscoring the complexity of optimizing computational pipelines for AMR diagnosis. Overall, graph-based modeling substantially enhances AMR diagnostic performance over conventional approaches, enabling scalable, low-cost and reproducible workflows with minimal expert input. These findings demonstrate the potential of GNNs to support nephropathologists in delivering more consistent and reliable diagnoses, with future work needed to refine feature representations and integrate multimodal data for broader clinical utility.

Graph neural networks

Antibody-mediated rejection

Whole slide imaging

Digital pathology

Deep learning

Nephropathology

© 2025 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.