Artificial Intelligence for Diagnostic Pathology in Nephrology

The study "Computational Characterization of Lymphocyte Topology on Whole Slide Images of Glomerular Diseases," 1 published in this issue of JASN, presents an ambitious and carefully designed computational pathology study aimed at characterizing lymphocyte spatial organization in glomerular diseases.Using whole slide pathology images from two clinical cohorts, the NEPhrotic syndrome sTUdy NEtwork (NEPTUNE) and Cure Glomerulonephropathy (CureGN) cohorts, the authors develop a multistep pipeline.Their computational method identifies lymphocytes in images, constructs cell graphs, defines dense and sparse "immune habitats," and extracts a number of "graph topology features" describing connectivity, clustering, and structural cohesion.These features are then linked to clinical outcomes, specifically a relevant decline in eGFR or kidney failure.Several findings stand out.First, from a technical standpoint, it is noteworthy that many of the topological features retain prognostic value even after adjustment for standard clinical covariates, suggesting that spatial immune architecture contains clinically relevant information that is not captured by conventional pathology or by simple lymphocyte density measures.This is very interesting because it provides a clear justification of using artificial intelligence (AI)-based assessment of pathology slides, as opposed to simple metrics such as cell counts.Second, the authors show that "sparse" and "dense" habitats contribute to clinical outcomes in distinct ways, where increased "connectivity" in dense regions appears associated with more severe inflammatory states.This finding is relevant because it shows that computationally defined patterns are interpretable in biologic terms and can provide new hypotheses in how we look at biologic processes, such as inflammation in the kidney.Third, the authors show that external validation, which in this case means training on NEPTUNE and testing on CureGN, reveals meaningful differences between cohorts.This phenomenon is called batch effect and is an important reminder that any image analysis model should always be tested on external datasets and