Nascent shifts in renal cellular metabolism, structure, and function due to chronic empagliflozin in prediabetic mice.

Sodium-glucose cotransporter, type 2 inhibitors (SGLT2i) are emerging as the gold standard for treatment of type 2 diabetes (T2D) with renal protective benefits independent of glucose lowering. We took a high-level approach to evaluate the effects of the SGLT2i, empagliflozin (EMPA) on renal metabolism and function in a prediabetic model of metabolic syndrome. Male and female 12-wk-old TallyHo (TH) mice, and their closest genetic lean strain (Swiss-Webster, SW) were treated with a high-milk-fat diet (HMFD) plus/minus EMPA (@0.01%) for 12-wk. Kidney weights and glomerular filtration rate were slightly increased by EMPA in the TH mice. Glomerular feature analysis by unsupervised clustering revealed sexually dimorphic clustering, and one unique cluster relating to EMPA. Periodic acid Schiff (PAS) positive areas, reflecting basement membranes and mesangium were slightly reduced by EMPA. Phasor-fluorescent life-time imaging (FLIM) of free-to-protein bound NADH in cortex showed a marginally greater reliance on oxidative phosphorylation with EMPA. Overall, net urine sodium, glucose, and albumin were slightly increased by EMPA. In TH, EMPA reduced the sodium phosphate cotransporter, type 2 (NaPi-2), but increased sodium hydrogen exchanger, type 3 (NHE3). These changes were absent or blunted in SW. EMPA led to changes in urine exosomal microRNA profile including, in females, enhanced levels of miRs 27a-3p, 190a-5p, and 196b-5p. Network analysis revealed "cancer pathways" and "FOXO signaling" as the major regulated pathways. Overall, EMPA treatment to prediabetic mice with limited renal disease resulted in modifications in renal metabolism, structure, and transport, which may preclude and underlie protection against kidney disease with developing T2D.NEW & NOTEWORTHY Renal protection afforded by sodium glucose transporter, type 2 inhibitors (SGLT2i), e.g., empagliflozin (EMPA) involves complex intertwined mechanisms. Using a novel mouse model of obesity with insulin resistance, the TallyHo/Jng (TH) mouse on a high-milk-fat diet (HMFD), we found subtle changes in metabolism including altered regulation of sodium transporters that line the renal tubule. New potential epigenetic determinants of metabolic changes relating to FOXO and cancer signaling pathways were elucidated from an altered urine exosomal microRNA signature.

NAD metabolism modulates inflammation and mitochondria function in diabetic kidney disease.

Diabetes mellitus is the leading cause of cardiovascular and renal disease in the United -States. Despite the beneficial interventions available for patients with diabetes, there remains a need for additional therapeutic targets and therapies in diabetic kidney disease (DKD). Inflammation and oxidative stress are increasingly recognized as important causes of renal diseases. Inflammation is closely associated with mitochondrial damage. The molecular connection between inflammation and mitochondrial metabolism remains to be elucidated. Recently, nicotinamide adenine nucleotide (NAD+) metabolism has been found to regulate immune function and inflammation. In the present studies, we tested the hypothesis that enhancing NAD metabolism could prevent inflammation in and progression of DKD. We found that treatment of db/db mice with type 2 diabetes with nicotinamide riboside (NR) prevented several manifestations of kidney dysfunction (i.e., albuminuria, increased urinary kidney injury marker-1 (KIM1) excretion, and pathologic changes). These effects were associated with decreased inflammation, at least in part via inhibiting the activation of the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) signaling pathway. An antagonist of the serum stimulator of interferon genes (STING) and whole-body STING deletion in diabetic mice showed similar renoprotection. Further analysis found that NR increased SIRT3 activity and improved mitochondrial function, which led to decreased mitochondrial DNA damage, a trigger for mitochondrial DNA leakage which activates the cGAS-STING pathway. Overall, these data show that NR supplementation boosted NAD metabolism to augment mitochondrial function, reducing inflammation and thereby preventing the progression of diabetic kidney disease.

Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis.

BACKGROUND: Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform. METHODS: A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools. RESULTS: The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables. CONCLUSIONS: ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

Computational Segmentation and Classification of Diabetic Glomerulosclerosis.

BACKGROUND: Pathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation. METHODS: We developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally quantify glomerular structure despite its complexity, we simplified it to three components consisting of nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive structures. We detected glomerular boundaries and nuclei from whole slide images using convolutional neural networks, and the remaining glomerular structures using an unsupervised technique developed expressly for this purpose. We defined a set of digital features which quantify the structural progression of DN, and a recurrent network architecture which processes these features into a classification. RESULTS: Our digital classification agreed with a senior pathologist whose classifications were used as ground truth with moderate Cohen's kappa κ = 0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists agreed with the digital classification with κ1 = 0.68, 95% interval [0.50, 0.86] and κ2 = 0.48, 95% interval [0.32, 0.64]. Our results suggest computational approaches are comparable to human visual classification methods, and can offer improved precision in clinical decision workflows. We detected glomerular boundaries from whole slide images with 0.93±0.04 balanced accuracy, glomerular nuclei with 0.94 sensitivity and 0.93 specificity, and glomerular structural components with 0.95 sensitivity and 0.99 specificity. CONCLUSIONS: Computationally derived, histologic image features hold significant diagnostic information that may augment clinical diagnostics.

Automated Reference Kidney Histomorphometry using a Panoptic Segmentation Neural Network Correlates to Patient Demographics and Creatinine.

Reference histomorphometric data of healthy human kidneys are lacking due to laborious quantitation requirements. We leveraged deep learning to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine in a multinational set of reference kidney tissue sections. A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in digitized images of 79 periodic acid-Schiff (PAS)-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were measured from the segmented classes. Regression analysis was used to determine the relationship of histomorphometric parameters with age, sex, and serum creatinine. The model achieved high segmentation performance for all test compartments. We found that the size and density of nephrons, arteries/arterioles, and the baseline level of interstitium vary significantly among healthy humans, with potentially large differences between subjects from different geographic locations. Nephron size in any region of the kidney was significantly dependent on patient creatinine. Slight differences in renal vasculature and interstitium were observed between sexes. Finally, glomerulosclerosis percentage increased and cortical density of arteries/arterioles decreased as a function of age. We show that precise measurements of kidney histomorphometric parameters can be automated. Even in reference kidney tissue sections with minimal pathologic changes, several histomorphometric parameters demonstrated significant correlation to patient demographics and serum creatinine. These robust tools support the feasibility of deep learning to increase efficiency and rigor in histomorphometric analysis and pave the way for future large-scale studies.

Discovery of Novel Digital Biomarkers for Type 2 Diabetic Nephropathy Classification via Integration of Urinary Proteomics and Pathology.

Background: The heterogeneous phenotype of diabetic nephropathy (DN) from type 2 diabetes complicates appropriate treatment approaches and outcome prediction. Kidney histology helps diagnose DN and predict its outcomes, and an artificial intelligence (AI)-based approach will maximize clinical utility of histopathological evaluation. Herein, we addressed whether AI-based integration of urine proteomics and image features improves DN classification and its outcome prediction, altogether augmenting and advancing pathology practice. Methods: We studied whole slide images (WSIs) of periodic acid-Schiff-stained kidney biopsies from 56 DN patients with associated urinary proteomics data. We identified urinary proteins differentially expressed in patients who developed end-stage kidney disease (ESKD) within two years of biopsy. Extending our previously published human-AI-loop pipeline, six renal sub-compartments were computationally segmented from each WSI. Hand-engineered image features for glomeruli and tubules, and urinary protein measurements, were used as inputs to deep-learning frameworks to predict ESKD outcome. Differential expression was correlated with digital image features using the Spearman rank sum coefficient. Results: A total of 45 urinary proteins were differentially detected in progressors, which was most predictive of ESKD (AUC=0.95), while tubular and glomerular features were less predictive (AUC=0.71 and AUC=0.63, respectively). Accordingly, a correlation map between canonical cell-type proteins, such as epidermal growth factor and secreted phosphoprotein 1, and AI-based image features was obtained, which supports previous pathobiological results. Conclusions: Computational method-based integration of urinary and image biomarkers may improve the pathophysiological understanding of DN progression as well as carry clinical implications in histopathological evaluation.

Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis.

A system for analysis of histopathology data within a pharmaceutical R&D environment has been developed with the intention of enabling interdisciplinary collaboration. State-of-the-art AI tools have been deployed as easy-to-use self-service modules within an open-source whole slide image viewing platform, so that non-data scientist users (e.g., clinicians) can utilize and evaluate pre-trained algorithms and retrieve quantitative results. The outputs of analysis are automatically cataloged in the database to track data provenance and can be viewed interactively on the slide as annotations or heatmaps. Commonly used models for analysis of whole slide images including segmentation, extraction of hand-engineered features for segmented regions, and slide-level classification using multi-instance learning are included and new models can be added as needed. The source code that supports running inference with these models internally is backed up by a robust CI/CD pipeline to ensure model versioning, robust testing, and seamless deployment of the latest models. Examples of the use of this system in a pharmaceutical development workflow include glomeruli segmentation, enumeration of podocyte count from WT-1 immuno-histochemistry, measurement of beta-1 integrin target engagement from immunofluorescence, digital glomerular phenotyping from periodic acid-Schiff histology, PD-L1 score prediction using multi-instance learning, and the deployment of the open-source Segment Anything model to speed up annotation.

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine.

Background: Reference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine (SCr) in a multinational set of reference kidney tissue sections. Methods: A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid-Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the relationship of histomorphometric parameters with age, sex, and SCr. Results: Our deep-learning model achieved high segmentation performance for all test compartments. The size and density of nephrons and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Nephron size was significantly dependent on SCr. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of age. Conclusions: Using deep learning, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics and SCr. Deep learning tools can increase the efficiency and rigor of histomorphometric analysis.

HistoLens: A generalizable tool for increasing accessibility and interpretability of quantitative analyses in digital pathology.

The incorporation of automated computational tools has a great amount of potential to positively influence the field of pathology. However, pathologists and regulatory agencies are reluctant to trust the output of complex models such as Convolutional Neural Networks (CNNs) due to their usual implementation as black-box tools. Increasing the interpretability of quantitative analyses is a critical line of research in order to increase the adoption of modern Machine Learning (ML) pipelines in clinical environments. Towards that goal, we present HistoLens, a Graphical User Interface (GUI) designed to facilitate quantitative assessments of datasets of annotated histological compartments. Additionally, we introduce the use of hand-engineered feature visualizations to highlight regions within each structure that contribute to particular feature values. These feature visualizations can then be paired with feature hierarchy determinations in order to view which regions within an image are significant to a particular sub-group within the dataset. As a use case, we analyzed a dataset of old and young mouse kidney sections with glomeruli annotated. We highlight some of the functional components within HistoLens that allow non-computational experts to efficiently navigate a new dataset as well as allowing for easier transition to downstream computational analyses.

Histo-Fetch - On-the-Fly Processing of Gigapixel Whole Slide Images Simplifies and Speeds Neural Network Training.

BACKGROUND: Training convolutional neural networks using pathology whole slide images (WSIs) is traditionally prefaced by the extraction of a training dataset of image patches. While effective, for large datasets of WSIs, this dataset preparation is inefficient. METHODS: We created a custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology WSIs for input to a neural network on-the-fly. We prefetch these patches as needed during network training, avoiding the need for WSI preparation such as chopping/tiling. RESULTS & CONCLUSIONS: We demonstrate the utility of this pipeline to perform artificial stain transfer and image generation using the popular networks CycleGAN and ProGAN, respectively. For a large WSI dataset, histo-fetch is 98.6% faster to start training and used 7535x less disk space.

Automated Tubular Morphometric Visualization for Whole Kidney Biopsy.

One of the strongest prognostic predictors of chronic kidney disease is interstitial fibrosis and tubular atrophy (IFTA). The ultimate goal of IFTA calculation is an estimation of the functional nephritic area. However, the clinical gold standard of estimation by pathologist is imprecise, primarily due to the overwhelming number of tubules sampled in a standard kidney biopsy. Artificial intelligence algorithms could provide significant benefit in this aspect as their high-throughput could identify and quantitatively measure thousands of tubules in mere minutes. Towards this goal, we use a custom panoptic convolutional network similar to Panoptic-DeepLab to detect tubules from 87 WSIs of biopsies from native diabetic kidneys and transplant kidneys. We measure 206 features on each tubule, including commonly understood features like tubular basement membrane thickness and tubular diameter. Finally, we have developed a tool which allows a user to select a range of tubule morphometric features to be highlighted in corresponding WSIs. The tool can also highlight tubules in WSI leveraging multiple morphometric features through selection of regions-of-interest in a uniform manifold approximation and projection plot.

Computational Integration of Renal Histology and Urinary Proteomics using Neural Networks.

Histological image data and molecular profiles provide context into renal condition. Often, a biopsy is drawn to diagnose or monitor a suspected kidney problem. However, molecular profiles can go beyond a pathologist's ability to see and diagnose. Using AI, we computationally incorporated urinary proteomic profiles with microstructural morphology from renal biopsy to investigate new and existing molecular links to image phenotypes. We studied whole slide images of periodic acid-Schiff stained renal biopsies from 56 DN patients matched with 2,038 proteins measured from each patient's urine. Using Seurat, we identified differentially expressed proteins in patients that developed end-stage renal disease within 2 years of biopsy. Glomeruli, globally sclerotic glomeruli, and tubules were segmented from WSI using our previously published HAIL pipeline. For each glomerulus, 315 handcrafted digital image features were measured, and for tubules, 207 features. We trained fully connected networks to predict urinary protein measurements that were differentially expressed between patients who did/ did not progress to ESRD within 2 years of biopsy. The input to this network was either glomerular or tubular histomorphological features in biopsy. Trained network weights were used as a proxy to rank which morphological features correlated most highly with specific urinary proteins. We identified significant image feature-protein pairs by ranking network weights by magnitude. We also looked at which features on average were most significant in predicting proteins. For both glomeruli and tubules, RGB color values and variance in PAS+ areas (specifically basement membrane for tubules) were, on average, more predictive of molecular profiles than other features. There is a strong connection between molecular profile and image phenotype, which can be elucidated through computational methods. These discovered links can provide insight to disease pathways, and discover new factors contributing to incidence and progression.

Endothelial Cell-Specific Molecule-1 Inhibits Albuminuria in Diabetic Mice.

Background: Diabetic kidney disease (DKD) is the most common cause of kidney failure in the world, and novel predictive biomarkers and molecular mechanisms of disease are needed. Endothelial cell-specific molecule-1 (Esm-1) is a secreted proteoglycan that attenuates inflammation. We previously identified that a glomerular deficiency of Esm-1 associates with more pronounced albuminuria and glomerular inflammation in DKD-susceptible relative to DKD-resistant mice, but its contribution to DKD remains unexplored. Methods: Using hydrodynamic tail-vein injection, we overexpress Esm-1 in DKD-susceptible DBA/2 mice and delete Esm-1 in DKD-resistant C57BL/6 mice to study the contribution of Esm-1 to DKD. We analyze clinical indices of DKD, leukocyte infiltration, podocytopenia, and extracellular matrix production. We also study transcriptomic changes to assess potential mechanisms of Esm-1 in glomeruli. Results: In DKD-susceptible mice, Esm-1 inversely correlates with albuminuria and glomerular leukocyte infiltration. We show that overexpression of Esm-1 reduces albuminuria and diabetes-induced podocyte injury, independent of changes in leukocyte infiltration. Using a complementary approach, we find that constitutive deletion of Esm-1 in DKD-resistant mice modestly increases the degree of diabetes-induced albuminuria versus wild-type controls. By glomerular RNAseq, we identify that Esm-1 attenuates expression of kidney disease-promoting and interferon (IFN)-related genes, including Ackr2 and Cxcl11. Conclusions: We demonstrate that, in DKD-susceptible mice, Esm-1 protects against diabetes-induced albuminuria and podocytopathy, possibly through select IFN signaling. Companion studies in patients with diabetes suggest a role of Esm-1 in human DKD.

Variant APOL1 protein in plasma associates with larger particles in humans and mouse models of kidney injury.

BACKGROUND: Genetic variants in apolipoprotein L1 (APOL1), a protein that protects humans from infection with African trypanosomes, explain a substantial proportion of the excess risk of chronic kidney disease affecting individuals with sub-Saharan ancestry. The mechanisms by which risk variants damage kidney cells remain incompletely understood. In preclinical models, APOL1 expressed in podocytes can lead to significant kidney injury. In humans, studies in kidney transplant suggest that the effects of APOL1 variants are predominantly driven by donor genotype. Less attention has been paid to a possible role for circulating APOL1 in kidney injury. METHODS: Using liquid chromatography-tandem mass spectrometry, the concentrations of APOL1 were measured in plasma and urine from participants in the Seattle Kidney Study. Asymmetric flow field-flow fractionation was used to evaluate the size of APOL1-containing lipoprotein particles in plasma. Transgenic mice that express wild-type or risk variant APOL1 from an albumin promoter were treated to cause kidney injury and evaluated for renal disease and pathology. RESULTS: In human participants, urine concentrations of APOL1 were correlated with plasma concentrations and reduced kidney function. Risk variant APOL1 was enriched in larger particles. In mice, circulating risk variant APOL1-G1 promoted kidney damage and reduced podocyte density without renal expression of APOL1. CONCLUSIONS: These results suggest that plasma APOL1 is dynamic and contributes to the progression of kidney disease in humans, which may have implications for treatment of APOL1-associated kidney disease and for kidney transplantation.

A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology.

Background: Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods: We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results: By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions: Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.

In Silico Multi-Compartment Detection Based on Multiplex Immunohistochemical Staining in Renal Pathology.

With the rapid advancement in multiplex tissue staining, computer hardware, and machine learning, computationally-based tools are becoming indispensable for the evaluation of digital histopathology. Historically, standard histochemical staining methods such as hematoxylin and eosin, periodic acid-Schiff, and trichrome have been the gold standard for microscopic tissue evaluation by pathologists, and therefore brightfield microscopy images derived from such stains are primarily used for developing computational pathology tools. However, these histochemical stains are nonspecific in terms of highlighting structures and cell types. In contrast, immunohistochemical stains use antibodies to specifically detect and quantify proteins, which can be used to specifically highlight structures and cell types of interest. Traditionally, such immunofluorescence-based methods are only able to simultaneously stain a limited number of target proteins/antigens, typically up to three channels. Fluorescence-based multiplex immunohistochemistry (mIHC) is a new technology that enables simultaneous localization and quantification of numerous proteins/antigens, allowing for the possibility to detect a wide range of histologic structures and cell types within tissue. However, this method is limited by cost, specialized equipment, technical expertise, and time. In this study, we implemented a deep learning-based pipeline to synthetically generate in silico mIHC images from brightfield images of tissue slides-stained with routinely used histochemical stains, in particular PAS. Our tool was trained using fluorescence-based mIHC images as the ground-truth. The proposed pipeline offers high contrast detection of structures in brightfield imaged tissue sections stained with standard histochemical stains. We demonstrate the performance of our pipeline by computationally detecting multiple compartments in kidney biopsies, including cell nuclei, collagen/fibrosis, distal tubules, proximal tubules, endothelial cells, and leukocytes, from PAS-stained tissue sections. Our work can be extended for other histologic structures and tissue types and can be used as a basis for future automated annotation of histologic structures and cell types without the added cost of actually generating mIHC slides.

Automated detection and quantification of Wilms' Tumor 1-positive cells in murine diabetic kidney disease.

In diabetic kidney disease (DKD), podocyte depletion, and the subsequent migration of parietal epithelial cells (PECs) to the tuft, is a precursor to progressive glomerular damage, but the limitations of brightfield microscopy currently preclude direct pathological quantitation of these cells. Here we present an automated approach to podocyte and PEC detection developed using kidney sections from mouse model emulating DKD, stained first for Wilms' Tumor 1 (WT1) (podocyte and PEC marker) by immunofluorescence, then post-stained with periodic acid-Schiff (PAS). A generative adversarial network (GAN)-based pipeline was used to translate these PAS-stained sections into WT1-labeled IF images, enabling in silico label-free podocyte and PEC identification in brightfield images. Our method detected WT1-positive cells with high sensitivity/specificity (0.87/0.92). Additionally, our algorithm performed with a higher Cohen's kappa (0.85) than the average manual identification by three renal pathologists (0.78). We propose that this pipeline will enable accurate detection of WT1-positive cells in research applications.

A Distributed System Improves Inter-Observer and AI Concordance in Annotating Interstitial Fibrosis and Tubular Atrophy.

Histologic examination of interstitial fibrosis and tubular atrophy (IFTA) is critical to determine the extent of irreversible kidney injury in renal disease. The current clinical standard involves pathologist's visual assessment of IFTA, which is prone to inter-observer variability. To address this diagnostic variability, we designed two case studies (CSs), including seven pathologists, using HistomicsTK- a distributed system developed by Kitware Inc. (Clifton Park, NY). Twenty-five whole slide images (WSIs) were classified into a training set of 21 and a validation set of four. The training set was composed of seven unique subsets, each provided to an individual pathologist along with four common WSIs from the validation set. In CS 1, all pathologists individually annotated IFTA in their respective slides. These annotations were then used to train a deep learning algorithm to computationally segment IFTA. In CS 2, manual and computational annotations from CS 1 were first reviewed by the annotators to improve concordance of IFTA annotation. Both the manual and computational annotation processes were then repeated as in CS1. The inter-observer concordance in the validation set was measured by Krippendorff's alpha (KA). The KA for the seven pathologists in CS1 was 0.62 with CI [0.57, 0.67], and after reviewing each other's annotations in CS2, 0.66 with CI [0.60, 0.72]. The respective CS1 and CS2 KA were 0.58 with CI [0.52, 0.64] and 0.63 with CI [0.56, 0.69] when including the deep learner as an eighth annotator. These results suggest that our designed annotation framework refines agreement of spatial annotation of IFTA and demonstrates a human-AI approach to significantly improve the development of computational models.

Generative modeling for renal microanatomy.

Generative adversarial networks (GANs) have received immense attention in the field of machine learning for their potential to learn high-dimensional and real data distribution. These methods do not rely on any assumptions about the data distribution of the input sample and can generate real-like samples from latent vector space based on unsupervised learning. In the medical field, particularly, in digital pathology expert annotation and availability of a large set of training data is costly and the study of manifestations of various diseases is based on visual examination of stained slides. In clinical practice, various staining information is required to improve the pathological diagnosis process. But when the sampled tissue to be examined is limited, the final diagnosis made by the pathologist is based on limited stain styles. These limitations can be overcome by studying the usability and reliability of generative models in the field of digital pathology. To understand the usability of the generative models, we synthesize in an unsupervised manner, high resolution renal microanatomical structures like renal glomerulus in thin tissue histology images using state-of-art architectures like Deep Convolutional Generative Adversarial Network (DCGAN) and Enhanced Super-Resolution Generative Adversarial Network (ESRGAN). Successful generation of such structures will lead to obtaining a large set of labeled data for further developing supervised algorithms for disease classification and understanding progression. Our study suggests while GAN is able to attain formalin fixed and paraffin embedded tissue image quality, GAN requires further prior knowledge as input to model intrinsic micro-anatomical details, such as capillary wall, urinary pole, nuclei placement, suggesting developing semi-supervised architectures by using these above details as prior information. Also, the generative models can be used to create an artificial effect of staining without physically tampering the histopathological slide. To demonstrate this, we use a CycleGAN network to transform Hematoxylin and eosin (H&E) stain to Periodic acid-Schiff (PAS) stain and Jones methenamine silver (JMS) stain to PAS stain. In this way GAN can be employed to translate different renal pathology stain styles when the relevant staining information is not available in the clinical settings.

Generative modeling for label-free glomerular modeling and classification.

Generative modeling using GANs has gained traction in machine learning literature, as training does not require labeled datasets. This is perfect for applications in biological datasets, where large labeled datasets are often difficult and expensive to acquire. However, generative models offer no easy way to encode real images into feature-sets, something that is desirable for network explainability and may yield potentially informative image features. For this reason, we test a VAE-GAN architecture for label-free modeling of glomerular structural features. We show that this network can generate realistic looking synthetic images, and be used to interpolate between images. To prove the biological relevance of the network encodings, we classify small-labeled sets of encoded glomeruli by biopsy Tervaert class and for the presence of sclerosis, obtaining a Cohen's kappa values of 0.87 and 0.78 respectfully.