Background: Chronic 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.
Methods: We 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.
Results: Applied 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.
Conclusions: DLRS-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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| http://dx.doi.org/10.1038/s43856-024-00708-3 | DOI Listing |
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11701080 | PMC |
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