Digital pathology and computational image analysis in nephropathology

The emergence of digital pathology — an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases. Developments in digital pathology and computational image analysis have the potential to identify new disease mechanisms, improve disease classification and prognostication, and ultimately aid the identification of targeted therapies. In this Review, the authors provide an outline of the digital ecosystem in nephropathology and describe potential applications and challenges associated with the emerging armamentarium of technologies for image analysis. Key points The introduction of digital pathology in clinical research, trials and practice has catalysed the development of novel machine-learning models for tissue interrogation with the potential to improve our ability to discover disease mechanisms, identify comprehensive, patient-specific phenotypes, classify kidney patients into clinically relevant categories, predict disease outcome and, ultimately, identify more targeted therapies. The development of computational image analysis tools for tissue interrogation has brought pathology to the forefront in this process of re-defining kidney diseases. The new nephropathology ecosystem offers several advantages over conventional path