Background: Interstitial fibrosis and tubular atrophy (IFTA), and density and shape of peritubular capillaries (PTCs), are independently prognostic of disease progression. This study aimed 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 NEPTUNE/CureGN participants were included: 112 minimal change disease, 134 focal segmental glomerulosclerosis, 61 membranous nephropathy, and 37 IgA nephropathy. A PAS-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 sub-regions. Participants were divided into training and testing datasets (1:1). Univariate models incorporating IFTA subregions, and IFS-PTC density were constructed. LASSO regression models were used to identify the top PTC features associated with disease progression stratified by IFTA and non-IFTA sub-regions. Machine learning models were built using the top PTC features in IFTA and non-IFTA sub-regions 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 associated with disease progression. The machine learning generated risk scores showed a significant association with disease progression on the independent testing set.

Conclusion: We uncovered previously underrecognized digital biomarkers of disease progression and the clinical relevance of the complex interplay between the status of the PTCs and the interstitial microenvironment.