68: Adapting Machine Learning Models Using Multisite Histopathology Data for Predicting Kidney Function

Thomas M. Menino Convention & Exhibition Center 

Manual visual assessment of donor renal biopsy whole-slide images (WSIs) remains the current standard for predicting post-transplant renal function. Computer-extracted image features (pathomics) from these WSIs can enable more accurate prediction of kidney transplant recipient outcomes. While prior pathomics work has focused on single-site data, little attention has been given to building predictive models that leverage data from multiple institutions. Multi-site pathomics data exhibits batch effects and feature distribution shifts from differences in scanners and tissue processing, which can limit cross-site generalizability. We compared lasso, ridge, elastic net, random forest and XGBoost models for binary and continuous renal function outcomes using pooled cohorts with ComBat-based harmonization. Preliminary results indicate that ComBat-harmonized features improved test MSE for eGFR by up to 40% and DGF AUC by 23% on average across models compared to prior single-site studies. These findings suggest pathomics-based models can support clinical decision-making for kidney transplant allocation across institutions.

Computational pathology

Data harmonization

Domain adaptation

Machine learning 

Section on Statistics in Imaging