ABSTRACT Pathologic assessment of kidney biopsy tissue remains the best predictor of adverse outcomes in patients with kidney diseases. These features are largely independent of disease etiology and are not well reflected in non- invasive tests (e.g. serum creatinine and albuminuria). Quantitative assessment of these parameters is time consuming and maybe flawed by heterogeneity of pathologic features within kidney tissue. We propose to evaluate and optimize computational image analysis approaches to support pathologic analysis of large pieces of cancer-free kidney tissue from patients who underwent nephrectomy which we have collected (n > 220). Computer-assisted analysis of glomerular phenotypes in these samples show that morphometric features in glomeruli without obvious pathology precede established pathologic changes. We hypothesize that evaluation of cancer-free kidney tissue will inform about subclinical damage in the remaining kidney which is associated with relevant pathologic and clinical parameters. We propose to assess glomeruli, arteries and tubuli, and determine the spatial inter-relationship of the assessed features within the kidney tissue. The examination of significantly larger pieces of kidney tissue than those obtained by needle biopsy allows to include 20 times more glomeruli (nephrectomy samples: avrg. 256 glomeruli/sample; needle biopsy: avrg. 13/sample) with the vast majority considered “normal appearing” as per standard pathologic criteria. In addition, these samples include a significant larger number of blood vessels (nephrectomy samples: avrg. 18 arteries/sample; needle biopsy: avrg. 1/sample) allowing a more robust evaluation of the vasculature. We propose to apply and optimize our detection and segmentation approach to detect glomeruli, arteries and tubular segments to train convolutional neural networks and use topological image analysis to automate the identification of visual and sub-visual features. In addition, we will assess the spatial relationship between individual features (glomeruli, arteries and tubular segments and features of the same category, i.e. globally sclerosed glomeruli, arteries with hyalinosis, atrophied tubuli) within the section. To determine reproducibility of our approach, we will assess a second tissue section from a separate part of the same samples. Specifically, we propose an algorithmic detection and characterization of kidney features using deep learning, a topological image analysis for discovery of novel sub-visual features in kidney tissue images and to determine spatial relatedness of these features. If successful, we will validate our analytical approach in future independent studies. For this purpose, we are already prospectively collecting kidney tissue and longitudinal clinical data from consented patients undergoing nephrectomies, allowing association of specific features with clinical relevant outcomes.