Validation of the Incremental Prognostic Value of Deceased Donation Pre-implantation Kidney Transplant Biopsies through Comprehensive Lesion Quantification with Deep Learning
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Background
Pre-implantation biopsy utility for deceased-donor kidney acceptance is controversial due to processing limitations and interpretive variability. We evaluated whether systematic histological assessment, including expert evaluation and quantification with the BanffNET automated deep learning system (17 lesions), predicts post-transplant failure under optimal laboratory conditions.
Methods
Biopsies from deceased donor kidneys (N=733) underwent optimized laboratory processing, whole slide imaging, expert Banff 2024 pathologic assessment, and BanffNET lesion quantification. We assessed their incremental predictive value beyond clinical characteristics for early graft dysfunction, longitudinal eGFR/UPCR trajectories, need for indication biopsies, and death-censored graft failure.
Results
Histopathology improved clinical model discrimination by 1-4% and increased explained variance for longitudinal trajectories by 1-2%. Continuous bivariate surface analysis, however, revealed that while histology adds marginal overall predictive accuracy, mapping continuous composite and individual BanffNET scores against the Kidney Donor Risk Index (KDRI) uncovers distinct topological risk gradients for early graft dysfunction and long-term graft failure.
Conclusion
While pre-implantation biopsies add minimal overall prognostic value, continuous bivariate mapping of KDRI and BanffNET scores uncovers distinct synergistic risk gradients for graft failure. These continuous clinical-histological surfaces could optimize organ salvage by precisely identifying viable high-KDRI kidneys lacking critical compounded damage and vice versa.
Lay Summary
Kidney transplantation is the optimal treatment for end-stage kidney disease, but donor shortages lead to long waiting times. Some donated kidneys are declined because biopsies taken during allocation show unfavorable findings. We assessed whether these biopsies improve prediction of post-transplant outcomes beyond routinely available donor and recipient characteristics. Using optimized tissue processing, expert kidney pathologist assessment, and BanffNET automated deep-learning analysis of digital whole-slide images, we found that adding biopsy findings resulted in only small improvements in outcome prediction. Thus, even under optimized conditions, pre-implantation biopsies provide limited additional information for assessing donor kidney suitability and may contribute to unnecessary organ discard. Automated digital analysis with BanffNET may help reduce variation between pathologists and improve histological assessment and risk stratification, particularly for kidneys from higher-risk donors.
