AI-Powered cellular morphometric biomarkers discovered in needle biopsy of prostatic cancer predict neoadjuvant androgen deprivation therapy response and prognosis: an international multicenter retrospective study.

Unlabelled: It is imperative to identify patients with prostate cancer (PCa) who will benefit from androgen receptor signaling inhibitors that can impact quality of life upon prolonged use. Using our extensively-validated artificial-intelligence technique: cellular morphometric biomarker via machine learning (CMB-ML), we identified 13 CMBs from whole slide images of needle biopsies from the trial specimens ( NCT02430480 , n=37) that accurately predicted response to neoadjuvant androgen deprivation therapy (NADT) (AUC: 0.980).

ANCA-Negative Pauci-Immune Glomerulonephritis Associated with Bartonella Endocarditis.

Kidney complications can occur due to infective endocarditis, one of which is glomerulonephritis. Most often, an immune complex or complement-mediated glomerulonephritis is seen on kidney biopsy. In a minor subset of cases, pauci-immune glomerulonephritis may be present.

Spatial proteomics of human diabetic kidney disease, from health to class III.

Aims/hypothesis: Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease in the USA and worldwide. Animal models have taught us much about DKD mechanisms, but translation of this knowledge into treatments for human disease has been slowed by the lag in our molecular understanding of human DKD.

Methods: Using our Spatial TissuE Proteomics (STEP) pipeline (comprising curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools), we imaged and analysed the expression of 21 proteins in 23 tissue sections from individuals with diabetes and healthy kidneys (n=5), compared to those with DKDIIA, IIA-B and IIB (n=2 each) and DKDIII (n=1).

exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies.

Background: Pathology reports contain key information about the patient's diagnosis as well as important gross and microscopic findings. These information-rich clinical reports offer an invaluable resource for clinical studies, but data extraction and analysis from such unstructured texts is often manual and tedious. While neural information retrieval systems (typically implemented as deep learning methods for natural language processing) are automatic and flexible, they typically require a large domain-specific text corpus for training, making them infeasible for many medical subdomains.

Improving quantification of renal fibrosis using .

Accurate quantification of renal fibrosis has profound importance in the assessment of chronic kidney disease (CKD). Visual analysis of a biopsy stained with trichrome under the microscope by a pathologist is the gold standard for evaluation of fibrosis. Trichrome helps to highlight collagen and ultimately interstitial fibrosis.

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.

Protein tyrosine phosphatase 1B is a regulator of alpha-actinin4 in the glomerular podocyte.

Glomerular podocytes are instrumental for the barrier function of the kidney, and podocyte injury contributes to proteinuria and the deterioration of renal function. Protein tyrosine phosphatase 1B (PTP1B) is an established metabolic regulator, and the inactivation of this phosphatase mitigates podocyte injury. However, there is a paucity of data regarding the substrates that mediate PTP1B actions in podocytes.

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.

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.

A tool for federated training of segmentation models on whole slide images.

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN-based models, but this is hindered by the logistical challenges of sharing medical data.

A cloud-based tool for federated segmentation of whole slide images.

It is commonly known that diverse datasets of WSIs are beneficial when training convolutional neural networks, however sharing medical data between institutions is often hindered by regulatory concerns. We have developed a cloud-based tool for federated WSI segmentation, allowing collaboration between institutions without the need to directly share data. To show the feasibility of federated learning on pathology data in the real world, We demonstrate this tool by segmenting IFTA from three institutions and show that keeping the three datasets separate does not hinder segmentation performance.

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.

PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images.

Background: Podocyte depletion precedes progressive glomerular damage in several kidney diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise.

Methods: We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning.

Deep learning-based transformation of H&E stained tissues into special stains.

Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson's Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections.

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 label-free podocyte and PEC identification in brightfield images.

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.

Atypical Antiglomerular Basement Membrane Disease in a Pediatric Patient Successfully Treated with Rituximab.

Classic antiglomerular basement membrane (anti-GBM) disease is an exceedingly rare but extremely aggressive form of glomerulonephritis, typically caused by autoantibodies directed against cryptic, conformational epitopes within the noncollagenous domain of the type IV collagen alpha-3 subunit. Pathologic diagnosis is established by the presence of strong, diffuse, linear staining for immunoglobulin on immunofluorescence microscopy. Recently, patients with atypical clinical and pathologic findings of anti-GBM disease have been described.

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.

Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts.

Background: Several groups have previously developed logistic regression models for predicting delayed graft function (DGF). In this study, we used an automated machine learning (ML) modeling pipeline to generate and optimize DGF prediction models en masse.

Methods: Deceased donor renal transplants at our institution from 2010 to 2018 were included.

COVID-19 Manifesting as Renal Allograft Dysfunction, Acute Pancreatitis, and Thrombotic Microangiopathy: A Case Report.

Coronavirus disease 2019 (COVID-19) is associated with high morbidity and mortality worldwide in both the general population and kidney transplant recipients. Acute kidney injury is a known complication of COVID-19 and appears to most commonly manifest as acute tubular injury on renal biopsy. Coagulopathy associated with COVID-19 is a known but poorly understood complication that has been reported to cause thrombotic microangiopathy on rare occasions in native kidneys of patients with COVID-19.