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Updated: Jun 20, 2026
Using 2-Photon Microscopy to Quantify the Effects of Chronic Unilateral Ureteral Obstruction on Glomerular Processes
Published on: March 4, 2022
Clinical Relevance of Computational Pathology Analysis of Interplay between Kidney Microvasculature and Interstitial
Yijiang Chen1,2, Bangchen Wang3,4, Dawit Demeke5
1Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
Key Points:
There is a modulatory effect between peritubular capillaries (PTCs) and areas of interstitial fibrosis and tubular atrophy (IFTA). The spatial architecture of non-IFTA PTCs on the cortex is significantly associated with glomerular disease progression. The shape of IFTA PTCs on the cortex is significantly associated with glomerular disease progression.
Background:
Interstitial fibrosis and tubular atrophy (IFTA) and density and shape of peritubular capillaries (PTCs) are independently prognostic of disease progression. The aim of this study was to identify novel digital biomarkers of disease progression and assess the clinical relevance of the interplay between a variety of PTC characteristics and their microenvironment in glomerular diseases.
Methods:
A total of 344 Nephrotic Syndrome Study Network/CureGN participants were included: 112 with minimal change disease, 134 with focal segmental glomerulosclerosis, 61 with membranous nephropathy, and 37 with IgA nephropathy. A periodic acid-Schiff-stained whole-slide image per patient was manually segmented for cortex, pre-, and mature IFTA. Interstitial fractional space (IFS) was computationally quantified. A deep learning model was applied to segment PTCs. Spatial and shape PTC pathomic features (230) were extracted from the cortex, IFTA, and non-IFTA subregions. Participants were divided into training and testing datasets (1:1). Univariate models incorporating IFTA subregions and IFS-PTC density were constructed. Least absolute shrinkage and selection operator regression models were used to identify the top PTC features associated with disease progression stratified by IFTA and non-IFTA subregions. Machine learning models were built using the top PTC features in IFTA and non-IFTA subregions to prognosticate disease progression.
Results:
PTC density in pre+mature IFTA and IFS, shape features in pre+mature IFTA, and spatial architecture features in the non-IFTA regions were associated with disease progression. The machine learning-generated risk scores showed a significant association with disease progression on the independent testing set.
Conclusions:
We uncovered previously under-recognized digital biomarkers of disease progression and the clinical relevance of the complex interplay between the status of the PTCs and interstitial microenvironment.
