Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights.
Publication/Presentation Date
10-8-2025
Abstract
UNLABELLED: Deep learning methods for image segmentation and classification in histopathology generally utilize supervised learning, relying on manually created labels for model development. Here, we applied a self-supervised framework to characterize kidney histology without the use of pathologist annotations, training on whole slide images to identify histomorphological phenotype clusters (HPCs) and create slide-level vector representations. HPCs developed in the training set were visually consistent when transferred to five diverse internal and external validation sets (1,421 WSIs in total). Specific HPCs were reproducibly associated with slide-level pathologist quantifications, such as interstitial fibrosis (AUC = 0.83). Additionally, hierarchical clustering of tissue patterns revealed patient groups related to kidney function and genotype, and specific HPCs predicted longitudinal kidney function decline. Overall, we demonstrated the translational application of a self-supervised framework to summarize distinct kidney tissue patterns with phenotypic and prognostic relevance.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1038/s41598-025-19193-2.
Volume
15
Issue
1
First Page
35131
Last Page
35131
ISSN
2045-2322
Published In/Presented At
Pandit, K., Coudray, N., Quiros, A. C., Surapaneni, A., Upadhyay, D., Vanguri, R. S., Hirohama, D., Mohandes, S., Schlosser, P., Thiessen-Philbrook, H., Wen, Y., Parikh, C. R., Rhee, E. P., Waikar, S. S., Schmidt, I., Rosenberg, A. Z., Palmer, M. B., Susztak, K., Grams, M. E., Tsirigos, A., … TRIDENT Study Investigators (2025). Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights. Scientific reports, 15(1), 35131. https://doi.org/10.1038/s41598-025-19193-2
Disciplines
Medicine and Health Sciences
PubMedID
41062686
Department(s)
Department of Medicine
Document Type
Article