Computational characterization of lymphocyte topology on whole slide images of glomerular diseases

The complexity of distribution of inflammatory cells in the kidney is not well captured by conventional semiquantitative visual assessment. This study aims to computationally quantify the topology of lymphocytic inflammation and tested its clinical relevance.

N=333 NEPTUNE/CureGN participants (N=155 focal segmental glomerulosclerosis (FSGS) and N=178 Minimal Change Disease (MCD) with available clinical/demographic data and 1 Hematoxylin & Eosin-stained whole slide image (WSI), were included. Deep learning models were applied to segment cortex and lymphocytes. Graph modeling, where nodes were defined as lymphocytes and edges as the spatial connections between cortical lymphocytes, were applied to all WSIs. We then developed a novel graph-based habitat clustering algorithm to identify dense vs. sparse lymphocytic habitats. From each habitat, 26 high-throughput quantitative pathomic features were extracted to capture cell density, connectivity, clustering, and centrality.

The association of these pathomic features with disease progression (40% eGFR decline or kidney replacement therapy) was assessed using LASSO-regularized Cox proportional hazards models. Clinical and demographic characteristics were added as potential confounders. Kaplan-Meier survival analysis with log-rank test was used to evaluate risk stratification. Two validation strategies were applied: (i) training on NEPTUNE with external validation on CureGN data, and (ii) using an 80/20 data partition of the combined datasets for training and validation, respectively.

Multivariable Cox models integrating clinical/demographic variables with graph features achieved validation concordance index of 0.736±0.072 in the CureGN external validation and 0.757±0.071 in the combined validation dataset. The average degree feature (overall connectivity) in dense habitat and k-core feature (clustering pattern strength) in sparse habitat revealed consistent association with clinical outcome.

The topological characterization of lymphocytic inflammation identifies immune habits, capturing the complexity of pattern of inflammation beyond human vision. These pathomic/topology signatures represent potential digital biomarkers that can enhance our ability to prognosticate/predict clinical outcome in MCD/FSGS.