Digital pathology in nephrology clinical trials, research, and pathology practice

Abstract

Purpose of review In this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine. Recent findings Recent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morphoomic taxonomies of renal diseases. Summary The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.

Magnetic resonance imaging biomarkers for chronic kidney disease: a position paper from the European Cooperation in Science and Technology Action PARENCHIMA

“…In clearly defined patient cohorts (both CKD and renal transplantation), individual or combinations of MRI biomarkers should be compared against quantitative renal histology techniques that measure important biological processes such as fibrosis (glomerulosclerosis score, extent of interstitial fibrosis, collagen accumulation), inflammation (density of inflammatory cells and cell subtypes), peritubular capillary density and podocyte loss. Such studies will be greatly aided by the use of digital pathology techniques and computer-driven methods for quantification [ 64 ]. In addition, combining MRI and renal biopsies for the determination of cortical volume and glomerular density offers the potential to provide estimates of glomerular numbers and single nephron GFR.…”

Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

“…and visual quantification by pathologists are time-consuming and limited by poor intra-and interreader reproducibility. [4][5][6][7] The introduction of digital pathology in nephrology clinical trials 8 has provided an unprecedented opportunity to test machine learning approaches for large-scale tissue quantification efforts. Standardization of pathology material acquisition has allowed worldwide consortia to establish digital pathology repositories containing thousands of digital renal biopsies for the evaluation of kidney diseases in adults and children, across diverse populations and pathology laboratories.…”

Digital pathology and computational image analysis in nephropathology

“…Digital pathology is a general term that refers to the assemblage of digital workflow and imaging solutions that are geared towards creating a digital image-based practice environment in which a whole slide image (WSI) or other digital image is acquired, managed, interpreted and searched for specific content. This new approach is being increasingly adopted in clinical trials 6 , for education purposes [7][8][9] and for clinical research 1,[10][11][12][13][14][15][16] . Since 2017, the FDA has approved the use of commercial WSI platforms, facilitating the use of digital pathology as a tool for primary diagnosis [17][18][19][20][21][22] .…”

“…The reproducibility of observations is also improved, facilitated by digital annotation 11,12,15,16 and computeraided quantitative assessment, which offers a numerical value to the observations and measurements of parameters (i.e. density, spatial distribution and relationships of histological primitives) that cannot be achieved by conventional analysis 1 . Thus, with the increasing role of…”