ArticleCommunications medicine2025
Deep learning-based histopathological assessment of tubulo-interstitial injury in chronic kidney diseases.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.Kidney international reports · 2026Review
- Reappraising Kidney Biopsy in Diabetic Kidney Disease: Histopathology, Clinical Course, and the Future of Precision Nephrology.Journal of clinical medicine · 2026Review
- Identification of Risk Factors for Renal Insufficiency in Primary Membranous Nephropathy.Saudi medical journal · 2026Article
- Article
- Normalized Raman Imaging for Studies of Tissue Physiology of the Kidney.bioRxiv : the preprint server for biology · 2025Article
- Leveraging advanced feature extraction for improved kidney biopsy segmentation.Frontiers in medicine · 2025Article
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Authors and funding
20 authors.
Funding
Abstract
backgroundChronic kidney disease (CKD) causes progressive and irreversible damage to the kidneys. Renal biopsies are essential for diagnosing the etiology and prognosis of CKD, while accurate quantification of tubulo-interstitial injuries from whole slide images (WSIs) of renal biopsy specimens is challenging with visual inspection alone.
methodsWe develop a deep learning-based method named DLRS to quantify interstitial fibrosis and inflammatory cell infiltration as tubulo-interstitial injury scores, from WSIs of renal biopsy specimens. DLRS segments WSIs into non-tissue areas, glomeruli, tubules, interstitium, and arteries, and detects interstitial nuclei. It then quantifies these tubulo-interstitial injury scores using the segmented tissues and detected nuclei.
resultsApplied to WSIs from 71 Japanese CKD patients with diabetic nephropathy or benign nephrosclerosis, DLRS-derived scores show concordance with nephrologists' evaluations. Notably, the DLRS-derived fibrosis score has a higher correlation with the estimated glomerular filtration rate (eGFR) at biopsy than scores from nephrologists' evaluations. Validated on WSIs from 28 Japanese tubulointerstitial nephritis patients and 49 European-ancestry patients with nephrosclerosis, DLRS-derived scores show a significant correlation with eGFR. In an expanded analysis of 238 Japanese CKD patients, including 167 from another hospital, deviations in eGFR from expected values based on DLRS-derived scores correlate with annual eGFR decline after biopsy. Inclusion of these deviations and DLRS-derived fibrosis scores improve predictions of the annual eGFR decline.
conclusionsDLRS-derived tubulo-interstitial injury scores are concordant with nephrologists' evaluations and correlated with eGFR across different populations and institutions. The effectiveness of DLRS-derived scores for predicting annual eGFR decline highlights the potential of DLRS as a predictor of renal prognosis.
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