Explainable Feature Embeddings from Histopathology Foundation Models: A Case Study for End Stage Kidney Disease Risk...

Foundational models (FMs) based on advanced neural network architectures have demonstrated improved performance in pathology image analysis across various organs due to their increased generalizability. However, their clinical adoption requires explainability, as their black box nature limits transparency. Understanding the specific features these models learn for a given downstream task is crucial for explainability and integrating FMs into clinical workflows more effectively. We propose a computational pipeline that enhances explainability by correlating domain-specific handcrafted features (HFs), with hidden features i.e., feature embeddings (FEs) from FMs. We correlate and combine HFs from Detectron 2 DeepLabv3+ segmentation with FEs from Prov-Gigapath (PG) and UNI FMs for improved explainability and performance. In this work, HFs are extracted from segmented functional tissue units, including arteries, tubules, globally sclerotic glomeruli, and non-globally sclerotic glomeruli. FEs are extracted at the tile and slide levels for PG and at the tile level for UNI. We use the Pearson correlation coefficient to identify significant correspondences between these feature sets. To evaluate our proposed methodology, we use 56 diabetic nephropathy kidney biopsy whole slide images (WSIs) from Seoul National University Hospital. The task is to predict end-stage kidney disease (ESKD) two years post-biopsy using leave-one-out cross-validation on 56 WSIs, with 16 from ESKD patients and 40 from non-ESKD patients. We combine top correlated features from FEs of FMs with HFs and train logistic regression (LR) and $k$ nearest neighbor ( $k\text{NN}$ ) classifiers. LR model trained on combined feature set improved accuracy, balanced accuracy, Matthew's correlation coefficient, F1-score, precision, and recall to 0.8393, 0.7938, 0.5993, 0.8377, 0.8367, 0.8393 respectively, when compared to LR and $k\text{NN}$ models trained on individual feature sets. PG excelled in specificity (1.000) and AUROC (0.8281), while UNI showed superior AUPRC (0.7813) performance. We also present feature explainability maps corresponding to each feature in FE.