Enhancing Predictive Accuracy of Allograft Outcomes: Beyond Traditional Clinical and Biopsy Scoring Methods.

Kidney transplantation is a critical intervention for managing end-stage renal disease, with post-transplant biopsies providing essential insights into graft function. This study aimed to develop and evaluate an automated system for classifying kidney biopsies taken immediately after transplantation to predict patient outcomes, specifically changes in estimated glomerular filtration rate (eGFR). We utilized a dataset of 56 Formalin-Fixed, Paraffin-Embedded (FFPE) whole slide images of time-zero biopsies, stained with Periodic Acid-Schiff (PAS), along with time series eGFR data recorded at 3 months, 6 months and 12 motnhs post-transplant. We employed ComPRePS (an in-house multicompartment segmentation tool) to analyze biopsy images, segmenting compartments such as cortical and medullary interstitium, non-globally and globally sclerotic glomeruli, tubules, and arteries/arterioles. Focus was placed on glomeruli, from which embeddings were extracted using a pretrained Vision Transformer (ViT) model (vit-base-patch16-224-in21k). These embeddings captured critical features for training an Artificial Neural Network (ANN) model to classify patients based on the percent change in eGFR from 3 months to 12 months, categorizing them into 'eGFR decline' and 'eGFR stable' groups. The proposed classification model achieved an accuracy of 0.73 in predicting eGFR changes. We validated these results by visualizing the two patient groups in a reduced dimensional domain using UMAP (Uniform Manifold Approximation and Projection), which revealed a clear distinction between the groups. This result underscores the potential of early biopsies for predicting long-term graft outcomes, enhancing patient management by providing early insights. We also trained a baseline ANN model using glomeruli hand-crafted features from ComPRePS, which achieved an accuracy of 0.55. Our proposed model achieved higher accuracy than baseline. Further research is needed to refine the model, expand the dataset, and validate findings across diverse populations to improve prediction accuracy and clinical applicability.