Introduction
Nephropathology is a highly specialised area of clinical diagnostic pathology that focuses on biopsies of non-neoplastic kidney diseases. The diagnostic workup of a kidney biopsy typically involves integrating information from many tissue sections, stains and imaging modalities (light-, fluorescence- and electron microscopy) to assess a variety of defined lesions or injury patterns. These are often combined in semi-quantitative histological scores that are relevant to both formulate a final diagnosis and provide valuable, sometimes actionable information to clinicians. Important examples of such scores are the Banff-Classification of kidney allograft pathology [1] or the Oxford-classification of IgA-nephropathy [2]. However, the usefulness of these scores might sometimes be limited due to inter-observer variability and time required to perform the scoring.
Like the rest of pathology [3], nephropathology is undergoing digitisation [4] and studies on the use of artificial intelligence (AI)-based systems in nephropathology are published at an increasing rate. Most research uses deep learning, a subdomain of AI-research that makes use of deep artificial neural networks [5]. A particular focus in computational nephropathology lies on extraction of understandable quantitative data from histology that are used in a variety of downstream analyses. Typically, precise segmentation of histological structures (i. e., delineation of their borders), such as glomeruli or tubular cross sections is initially targeted. Segmentation then forms the basis for extracting structure related data, such as the area covered by, shape of, or the number of specific cells within the segmented structure. The next section highlights selected studies performing such analyses in kidney biopsies, focusing on recent advances.
Quantifying kidney histology using deep learning
The first multiclass segmentation model for nephropathology was trained to delineate several structures (glomeruli, tubuli, vessels and interstitium) with several classes (e. g., sclerotic and non-sclerotic glomeruli) in kidney allograft biopsies [6]. Data derived from segmented structures correlated well with pathologist derived Banff-lesion scores, illustrating the relevance of this approach. Studies performing segmentation of other structures, particularly peritubular capillaries [7] and kidneys from animal models [8] quickly followed, providing similar quantitative data extraction.
The downstream analysis of such datasets is particularly interesting when relevant clinical information is available. It has been shown that morphometric data derived from biopsies of IgA-nephropathy cases are independent predictors of renal long-term survival. A morphometric disease progression from healthy to end stage kidney disease can be inferred from the same data by performing a pseudotime trajectory analysis, a method developed for single cell transcriptomics data [9]. By going back to the images of the individual structure along the trajectory it is possible to assess its plausibility and identify specific morphological variations that might not directly be obvious from the structure related data (e. g., changes in the shape of the glomerular tuft might correspond to segmental sclerosis or cellular crescents). As the number of features extracted per histological structure increases, their analysis becomes more complicated and analysis techniques from the OMICS sciences become relevant for quantitative histopathology. This opens a novel research field within computational pathology: pathomics (Figure 1).
