A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images

Abstract

Histopathology foundation models (HFMs), pretrained on large-scale cancer datasets, have advanced computational pathology. However, their applicability to non-cancerous chronic kidney disease remains underexplored, despite coexistence of renal pathology with malignancies such as renal cell and urothelial carcinoma. We systematically evaluate 11 publicly available HFMs across 11 kidney-specific downstream tasks spanning multiple stains (PAS, H&E, PASM, and IHC), spatial scales (tile and slide-level), task types (classification, regression, and copy detection), and clinical objectives, including detection, diagnosis, and prognosis. Tile-level performance is assessed using repeated stratified group cross-validation, while slide-level tasks are evaluated using repeated nested stratified cross-validation. Statistical significance is examined using Friedman test followed by pairwise Wilcoxon signed-rank testing with Holm-Bonferroni correction and compact letter display visualization. To promote reproducibility, we release an open-source Python package, kidney-hfm-eval, available at https://pypi.org/project/kidney-hfm-eval/ , that reproduces the evaluation pipelines. Results show moderate to strong performance on tasks driven by coarse meso-scale renal morphology, including diagnostic classification and detection of prominent structural alterations. In contrast, performance consistently declines for tasks requiring fine-grained microstructural discrimination, complex biological phenotypes, or slide-level prognostic inference, largely independent of stain type. Overall, current HFMs appear to encode predominantly static meso-scale representations and may have limited capacity to capture subtle renal pathology or prognosis-related signals. Our results highlight the need for kidney-specific, multi-stain, and multimodal foundation models to support clinically reliable decision-making in nephrology.

Figures (12)

• Figure 1: Representative histopathology examples of glomerulosclerosis.
• Figure 1: Pipeline for generating ground truth labels for tubules using spatial transcriptomics and histology. (A) H&E-stained histology image patch with segmented tubules ($T_i$) and Visium spatial transcriptomics spot locations ($S_j$). (B) Seurat-based cell deconvolution workflow: kidney reference single-cell RNA-seq data (cell-by-gene matrix with cell-type annotations) is integrated with Visium spot-level gene expression (spot-by-gene matrix) to estimate cell-type proportions per spatial spot. (C) Deconvolved Visium spots overlaid on the histology image, each showing estimated proportions of multiple cell types. (D) Assignment of each spatial spot to its nearest tubule segment transferring spot-specific cell-type proportions to the corresponding tubule. (E) Final tubule-level ground truth labels, obtained by taking weighted ($w_i$) combination of $y_i$ where each tubule ($T_i$) is associated with its aggregated cell-type proportion vector ($\mathbf{y}_{T_k}$) for downstream task.
• Figure 2: Representative histopathology examples of GBM spike detection.
• Figure 2: Heatmap plots showing the p-values obtained from comparing the Matthews correlation coefficient (MCC) values across HFMs for multiple downstream tasks. Each subplot represents a specific task and probing method: (a)–(b) globally vs. non-globally sclerotic glomeruli classification using logistic regression (LR) and $k$NN, (c)–(d) GBM spike vs. no-GBM spike classification (LR, $k$NN), (e)–(f) normal vs. abnormal tubule classification (LR, $k$NN), (g)–(h) inflammation vs. non-inflammation classification (LR, $k$NN), (i)–(j) arteriolar stenosis multi-class classification (LR, $k$NN), (k) cell type estimation (ridge regression) (i) diabetic nephropathy vs. control classification using MIL. (m) membranous nephropathy treatment-response prediction using multiple-instance learning (MIL), (n) renal transplant eGFR decline classification using MIL, (o) renal transplant eGFR prediction using MIL, and Wider violins indicate greater performance variability, and higher median lines indicate more consistent model performance across cross-validation replicates.
• Figure 3: Representative histopathology examples reference and abnormal tubules.