Morphometry Predicts Early GFR Change in Primary Proteinuric Glomerulopathies: A Longitudinal Cohort Study Using Generalized Estimating Equations

Morphometric analysis of chronicity on kidney biopsy: a useful prognostic exercise

Chronic changes on kidney biopsy specimens include increasing amounts of arteriosclerosis, glomerulosclerosis, interstitial fibrosis and tubular atrophy, enlarged nephron size, and reduced nephron number. These chronic changes are difficult to accurately assess by visual inspection but are reasonably quantified using morphometry. This review describes the various patient populations that have undergone morphometric analysis of kidney biopsies. The common approaches to morphometric analysis are described. The chronic kidney disease outcomes associated with various chronic changes by morphometry are also summarized. Morphometry enriches the characterization of chronicity on a kidney biopsy and this can supplement the pathologist's diagnosis. Artificial intelligence image processing tools are needed to automate the annotations needed for practical morphometric analysis of kidney biopsy specimens in routine clinical care.

Comparative evaluation of glomerular morphometric techniques reveals differential technical artifacts between focal segmental glomerulosclerosis and normal glomeruli

Morphometric estimates of mean or individual glomerular volume (MGV, IGV) have biological implications, over and above qualitative histologic data. However, morphometry is time-consuming and requires expertise limiting its utility in clinical cases. We evaluated MGV and IGV using plastic- and paraffin-embedded tissue from 10 control and 10 focal segmental glomerulosclerosis (FSGS) mice (aging and 5/6th nephrectomy models) using the gold standard Cavalieri (Cav) method versus the 2-profile and Weibel-Gomez (WG) methods and a novel 3-profile method. We compared accuracy, bias and precision, and quantified results obtained when sampling differing numbers of glomeruli. In both FSGS and controls, we identified an acceptable precision for MGV of 10-glomerular sampling versus 20-glomerular sampling using the Cav method, while 5-glomerular sampling was less precise. In plastic tissue, 2- or 3-profile MGVs showed greater concordance with MGV when using Cav, versus MGV with WG. IGV comparisons using the same glomeruli reported a consistent underestimation bias with both 2- or 3-profile methods versus the Cav method. FSGS glomeruli showed wider variations in bias estimation than controls. Our 3-profile method offered incremental benefit to the 2-profile method in both IGV and MGV estimation (improved correlation coefficient, Lin's concordance and reduced bias). In our control animals, we quantified a shrinkage artifact of 52% from tissue processed for paraffin-embedded versus plastic-embedded tissue. FSGS glomeruli showed overall reduced shrinkage albeit with variable artifact signifying periglomerular/glomerular fibrosis. A novel 3-profile method offers slightly improved concordance with reduced bias versus 2-profile. Our findings have implications for future studies using glomerular morphometry.

AMPK mediates regulation of glomerular volume and podocyte survival

Herein, we report that Shroom3 knockdown, via Fyn inhibition, induced albuminuria with foot process effacement (FPE) without focal segmental glomerulosclerosis (FSGS) or podocytopenia. Interestingly, knockdown mice had reduced podocyte volumes. Human minimal change disease (MCD), where podocyte Fyn inactivation was reported, also showed lower glomerular volumes than FSGS. We hypothesized that lower glomerular volume prevented the progression to podocytopenia. To test this hypothesis, we utilized unilateral and 5/6th nephrectomy models in Shroom3-KD mice. Knockdown mice exhibited less glomerular and podocyte hypertrophy after nephrectomy. FYN-knockdown podocytes had similar reductions in podocyte volume, implying that Fyn was downstream of Shroom3. Using SHROOM3 or FYN knockdown, we confirmed reduced podocyte protein content, along with significantly increased phosphorylated AMPK, a negative regulator of anabolism. AMPK activation resulted from increased cytoplasmic redistribution of LKB1 in podocytes. Inhibition of AMPK abolished the reduction in glomerular volume and induced podocytopenia in mice with FPE, suggesting a protective role for AMPK activation. In agreement with this, treatment of glomerular injury models with AMPK activators restricted glomerular volume, podocytopenia, and progression to FSGS. Glomerular transcriptomes from MCD biopsies also showed significant enrichment of Fyn inactivation and Ampk activation versus FSGS glomeruli. In summary, we demonstrated the important role of AMPK in glomerular volume regulation and podocyte survival. Our data suggest that AMPK activation adaptively regulates glomerular volume to prevent podocytopenia in the context of podocyte injury.

A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses

Computational reproducibility is a corner stone for sound and credible research. Especially in complex statistical analyses-such as the analysis of longitudinal data-reproducing results is far from simple, especially if no source code is available. In this work we aimed to reproduce analyses of longitudinal data of 11 articles published in PLOS ONE. Inclusion criteria were the availability of data and author consent. We investigated the types of methods and software used and whether we were able to reproduce the data analysis using open source software. Most articles provided overview tables and simple visualisations. Generalised Estimating Equations (GEEs) were the most popular statistical models among the selected articles. Only one article used open source software and only one published part of the analysis code. Replication was difficult in most cases and required reverse engineering of results or contacting the authors. For three articles we were not able to reproduce the results, for another two only parts of them. For all but two articles we had to contact the authors to be able to reproduce the results. Our main learning is that reproducing papers is difficult if no code is supplied and leads to a high burden for those conducting the reproductions. Open data policies in journals are good, but to truly boost reproducibility we suggest adding open code policies.

AI applications in renal pathology

The explosive growth of artificial intelligence (AI) technologies, especially deep learning methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare. New applications of AI to renal pathology have recently become available, driven by the successful AI deployments in digital pathology. However, synergetic developments of renal pathology and AI require close interdisciplinary collaborations between computer scientists and renal pathologists. Computer scientists should understand that not every AI innovation is translatable to renal pathology, while renal pathologists should capture high-level principles of the relevant AI technologies. Herein, we provide an integrated review on current and possible future applications in AI-assisted renal pathology, by including perspectives from computer scientists and renal pathologists. First, the standard stages, from data collection to analysis, in full-stack AI-assisted renal pathology studies are reviewed. Second, representative renal pathology-optimized AI techniques are introduced. Last, we review current clinical AI applications, as well as promising future applications with the recent advances in AI.

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.

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

The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined 125 biopsies for Minimal Change Disease collected across 29 NEPTUNE enrolling centers along with 459 whole slide images stained with Hematoxylin & Eosin (125), Periodic Acid Schiff (125), Silver (102), and Trichrome (107) divided into training, validation and testing sets (ratio 6:1:3). Histologic structures were manually segmented (30048 total annotations) by five nephropathologists. Twenty deep learning models were trained with optimal digital magnification across the structures and stains. Periodic Acid Schiff-stained whole slide images yielded the best concordance between pathologists and deep learning segmentation across all structures (F-scores: 0.93 for glomerular tufts, 0.94 for glomerular tuft plus Bowman’s capsule, 0.91 for proximal tubules, 0.93 for distal tubular segments, 0.81 for peritubular capillaries, and 0.85 for arteries and afferent arterioles). Optimal digital magnifications were 5X for glomerular tuft/tuft plus Bowman’s capsule, 10X for proximal/distal tubule, arteries and afferent arterioles, and 40X for peritubular capillaries. Silver stained whole slide images yielded the worst deep learning performance. Thus, this largest study to date adapted deep learning for the segmentation of kidney histologic structures across multiple stains and pathology laboratories. All data used for training and testing and a detailed online tutorial will be publicly available.

Using computer-assisted morphometrics of 5-year biopsies to identify biomarkers of late renal allograft loss

The current Banff scoring system was not developed to predict graft loss and may not be ideal for use in clinical trials aimed at improving allograft survival. We hypothesized that scoring histologic features of digitized renal allograft biopsies using a continuous, more objective, computer-assisted morphometric (CAM) system might be more predictive of graft loss. We performed a nested case-control study in kidney transplant recipients with a surveillance biopsy obtained 5 years after transplantation. Patients that developed death-censored graft loss (n = 67) were 2:1 matched on age, gender, and follow-up time to controls with surviving grafts (n = 134). The risk of graft loss was compared between CAM-based models vs a model based on Banff scores. Both Banff and CAM identified chronic lesions associated with graft loss (chronic glomerulopathy, arteriolar hyalinosis, and mesangial expansion). However, the CAM-based models predicted graft loss better than the Banff-based model, both overall (c-statistic 0.754 vs 0.705, P < .001), and in biopsies without chronic glomerulopathy (c-statistic 0.738 vs 0.661, P < .001) where it identified more features predictive of graft loss (% luminal stenosis and % mesangial expansion). Using 5-year renal allograft surveillance biopsies, CAM-based models predict graft loss better than Banff models and might be developed into biomarkers for future clinical trials.

Interstitial fibrosis scored on whole-slide digital imaging of kidney biopsies is a predictor of outcome in proteinuric glomerulopathies

Background

Interstitial fibrosis (IF), tubular atrophy (TA) and interstitial inflammation (II) are known determinants of progression of renal disease. Standardized quantification of these features could add value to current classification of glomerulopathies.

Methods

We studied 315 participants in the Nephrotic Syndrome Study Network (NEPTUNE) study, including biopsy-proven minimal change disease (MCD = 98), focal segmental glomerulosclerosis (FSGS = 121), membranous nephropathy (MN = 59) and IgA nephropathy (IgAN = 37). Cortical IF, TA and II were quantified (%) on digitized whole-slide biopsy images, by five pathologists with high inter-reader agreement (intra-class correlation coefficient >0.8). Tubulointerstitial messenger RNA expression was measured in a subset of patients. Multivariable Cox proportional hazards models were fit to assess association of IF with the composite of 40% decline in estimated glomerular filtration rate (eGFR) and end-stage renal disease (ESRD) and separately as well, and with complete remission (CR) of proteinuria.

Results

IF was highly correlated with TA (P < 0.001) and II (P < 0.001). Median IF varied by diagnosis: FSGS 17, IgAN 21, MN 7, MCD 1 (P < 0.001). IF was strongly correlated with baseline eGFR (P < 0.001) and proteinuria (P = 0.002). After adjusting for clinical pathologic diagnosis, age, race, global glomerulosclerosis, baseline proteinuria, eGFR and medications, each 10% increase in IF was associated with a hazard ratio of 1.29 (P < 0.03) for ESRD/40% eGFR decline, but was not significantly associated with CR. A total of 981 genes were significantly correlated with IF (|r| > 0.4, false discovery rate (FDR) < 0.01), including upstream regulators such as tumor necrosis factor, interferon gamma (IFN-gamma), and transforming growth factor beta 1 (TGF-B1), and signaling pathways for antigen presentation and hepatic fibrosis.

Conclusions

The degree of IF is associated with risk of eGFR decline across different types of proteinuric glomerulopathy, correlates with inflammatory and fibrotic gene expression, and may have predictive value in assessing risk of progression.

CureGN Study Rationale, Design, and Methods: Establishing a Large Prospective Observational Study of Glomerular Disease

RATIONALE & OBJECTIVES

Glomerular diseases, including minimal change disease, focal segmental glomerulosclerosis, membranous nephropathy, and immunoglobulin A (IgA) nephropathy, share clinical presentations, yet result from multiple biological mechanisms. Challenges to identifying underlying mechanisms, biomarkers, and new therapies include the rarity of each diagnosis and slow progression, often requiring decades to measure the effectiveness of interventions to prevent end-stage kidney disease (ESKD) or death.

STUDY DESIGN

Multicenter prospective cohort study.

SETTING & PARTICIPANTS

Cure Glomerulonephropathy (CureGN) will enroll 2,400 children and adults with minimal change disease, focal segmental glomerulosclerosis, membranous nephropathy, or IgA nephropathy (including IgA vasculitis) and a first diagnostic kidney biopsy within 5 years. Patients with ESKD and those with secondary causes of glomerular disease are excluded.

EXPOSURES

Clinical data, including medical history, medications, family history, and patient-reported outcomes, are obtained, along with a digital archive of kidney biopsy images and blood and urine specimens at study visits aligned with clinical care 1 to 4 times per year.

OUTCOMES

Patients are followed up for changes in estimated glomerular filtration rate, disease activity, ESKD, and death and for nonrenal complications of disease and treatment, including infection, malignancy, cardiovascular, and thromboembolic events.

ANALYTICAL APPROACH

The study design supports multiple longitudinal analyses leveraging the diverse data domains of CureGN and its ancillary program. At 2,400 patients and an average of 2 years' initial follow-up, CureGN has 80% power to detect an HR of 1.4 to 1.9 for proteinuria remission and a mean difference of 2.1 to 3.0mL/min/1.73m² in estimated glomerular filtration rate per year.

LIMITATIONS

Current follow-up can only detect large differences in ESKD and death outcomes.

CONCLUSIONS

Study infrastructure will support a broad range of scientific approaches to identify mechanistically distinct subgroups, identify accurate biomarkers of disease activity and progression, delineate disease-specific treatment targets, and inform future therapeutic trials. CureGN is expected to be among the largest prospective studies of children and adults with glomerular disease, with a broad goal to lessen disease burden and improve outcomes.

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

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 morpho-omic 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.

Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology

The introduction of digital pathology to nephrology provides a platform for the development of new methodologies and protocols for visual, morphometric and computer-aided assessment of renal biopsies. Application of digital imaging to pathology made substantial progress over the past decade; it is now in use for education, clinical trials and translational research. Digital pathology evolved as a valuable tool to generate comprehensive structural information in digital form, a key prerequisite for achieving precision pathology for computational biology. The application of this new technology on an international scale is driving novel methods for collaborations, providing unique opportunities but also challenges. Standardization of methods needs to be rigorously evaluated and applied at each step, from specimen processing to scanning, uploading into digital repositories, morphologic, morphometric and computer-aided assessment, data collection and analysis. In this review, we discuss the status and opportunities created by the application of digital imaging to precision nephropathology, and present a vision for the near future.