一种针对肾数字病理图像的病理基础模型综合基准测试
A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images
AI总结：
本文评估了11种公开的病理基础模型在11个肾脏特定下游任务中的表现，揭示了其在肾病诊断中的适用性及改进方向。
摘要：
病理基础模型（HFMs）在大规模癌症数据集上预训练，已推动计算病理学发展。然而，其在非癌性慢性肾病中的适用性仍待探索。本文系统评估了11种公开的HFMs在11个肾脏特定下游任务中的表现，涵盖多种染色、空间尺度、任务类型和临床目标。Tile级性能通过重复分层组交叉验证评估，而滑片级任务通过重复嵌套分层交叉验证评估。统计显著性通过Friedman检验和配对Wilcoxon符号秩检验（Holm-Bonferroni校正）分析。为促进可重复性，我们发布了开源Python包kidney-hfm-eval，可复现评估流程。结果表明，HFMs在驱动粗粒度肾形态的任务中表现中等至良好，但在需要细粒度微结构辨别、复杂生物表型或滑片级预后推断的任务中表现下降。总体而言，当前HFMs似乎主要编码静态中粒度表示，可能难以捕捉细微肾病或预后信号。本研究强调了开发针对肾脏、多染色和多模态的基础模型的必要性，以支持临床可靠的肾病决策。
英文摘要：
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.
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