Abstract
Semiquantitative histological scoring systems are frequently used in nephropathology. In computational nephropathology, the focus is on generating quantitative data from histology (so-called pathomics). Several recent studies have collected such data using next-generation morphometry (NGM) based on segmentations by artificial neural networks and investigated their usability for various clinical or diagnostic purposes. To present an overview of the current state of studies regarding renal pathomics and to identify current challenges and potential solutions. Due to the literature restriction (maximum of 30references), studies were selected based on adatabase search that processed as much data as possible, used innovative methodologies, and/or were ideally multicentric in design. Pathomics studies in the kidney have impressively demonstrated that morphometric data are useful clinically (for example, for prognosis assessment) and translationally. Further development of NGM requires overcoming some challenges, including better standardization and generation of prospective evidence.
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