Investigating quantitative histological characteristics in renal pathology using HistoLens.

HistoLens is an open-source graphical user interface developed using MATLAB AppDesigner for visual and quantitative analysis of histological datasets. HistoLens enables users to interrogate sets of digitally annotated whole slide images to efficiently characterize histological differences between disease and experimental groups. Users can dynamically visualize the distribution of 448 hand-engineered features quantifying color, texture, morphology, and distribution across microanatomic sub-compartments.

: A web-based application for in-depth exploration of multi-omics data with brightfield histology.

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed (unctional nit tate dentificati in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images.

Erratum: 89 Bridging Cell Biology and Engineering Sciences: Interdisciplinary Team-based Training in Computational Pathology - ERRATUM.

[This corrects the article DOI: 10.1017/cts.2023.

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.

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.

Integrating image analysis with single cell RNA-seq data to study podocyte-specific changes in diabetic kidney disease.

Podocyte injury plays a crucial role in the progression of diabetic kidney disease (DKD). Injured podocytes demonstrate variations in nuclear shape and chromatin distribution. These morphometric changes have not yet been quantified in podocytes.

: 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.

Lupus podocytopathy superimposed on diabetic glomerulosclerosis: A case report.

Rationale: Lupus podocytopathy (LP) is an entity that is increasingly being reported in the literature on systemic lupus erythematosus (SLE). LP is characterized by nephrotic syndrome in SLE patients with diffuse glomerular podocyte foot process effacement and no immune complex deposits along the capillary loops. Histologically, LP typically mimics minimal change disease or primary focal segmental glomerulosclerosis (FSGS) on a background of ISN/RPS class I or II lupus nephritis.

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.

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.

The New York State SARS-CoV-2 Testing Consortium: Regional Communication in Response to the COVID-19 Pandemic.

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2, created an unprecedented need for comprehensive laboratory testing of populations, in order to meet the needs of medical practice and to guide the management and functioning of our society. With the greater New York metropolitan area as an epicenter of this pandemic beginning in March 2020, a consortium of laboratory leaders from the assembled New York academic medical institutions was formed to help identify and solve the challenges of deploying testing. This report brings forward the experience of this consortium, based on the real-world challenges which we encountered in testing patients and in supporting the recovery effort to reestablish the health care workplace.

Probabilistic modeling of Diabetic Nephropathy progression.

Diabetic Nephropathy (DN) progression is stratified into several stages with different levels of proteinuria, albuminuria, and physical characteristics as observed by pathologists. These physical changes are primarily visible within a patient's glomeruli which function as filtration units for blood returning for oxygenation. As DN stage increases, it is possible to observe the thickening of the glomerular basement membrane, expansion of the mesangium, and development of nodular sclerosis.

Neutrophil Extracellular Traps (NETs): An unexplored territory in renal pathobiology, a pilot computational study.

In the age of modern medicine and artificial intelligence, image analysis and machine learning have revolutionized diagnostic pathology, facilitating the development of computer aided diagnostics (CADs) which circumvent prevalent diagnostic challenges. Although CADs will expedite and improve the precision of clinical workflow, their prognostic potential, when paired with clinical outcome data, remains indeterminate. In high impact renal diseases, such as diabetic nephropathy and lupus nephritis (LN), progression often occurs rapidly and without immediate detection, due to the subtlety of structural changes in transient disease states.

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.

The Presence and Location of Podocytes in Glomeruli as Affected by Diabetes Mellitus.

The primary purpose of the kidney, specifically the glomerulus, is filtration. Filtration is accomplished through the glomerular filtration barrier, which consists of the fenestrated endothelium, glomerular basement membrane, and specialized epithelial cells called podocytes. In pathologic states, such as Diabetes Mellitus (DM) and diabetic kidney disease (DKD), variable glomerular conditions result in podocyte injury and depletion, followed by progressive glomerular injury and DKD progression.

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.

An integrated iterative annotation technique for easing neural network training in medical image analysis.

Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer.

Examining Structural Patterns and Causality in Diabetic Nephropathy using inter-Glomerular Distance and Bayesian Graphical Models.

In diabetic nephropathy (DN), hyperglycemia drives a progressive thickening of glomerular filtration surfaces, increased cell proliferation as well as mesangial expansion and a constriction of capillary lumens. This leads to progressive structural changes inside the Glomeruli. In this work, we make a study of structural glomerular changes in DN from a graph-theoretic standpoint, using features extracted from Minimal Spanning Trees (MSTs) constructed over intercellular distances in order to classify the "packing signatures" of different DN stages.

Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists.

Context.—: Advancements in genomic, computing, and imaging technology have spurred new opportunities to use quantitative image analysis (QIA) for diagnostic testing.

Objective.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

Background: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive.

Methods: In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test.

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels).

Automated erythrocyte detection and classification from whole slide images.

Blood smear is a crucial diagnostic aid. Quantification of both solitary and overlapping erythrocytes within these smears, directly from their whole slide images (WSIs), remains a challenge. Existing software designed to accomplish the computationally extensive task of hematological WSI analysis is too expensive and is widely unavailable.

Outsourcing of Academic Clinical Laboratories: Experiences and Lessons From the Association of Pathology Chairs Laboratory Outsourcing Survey.

American hospitals are increasingly turning to service outsourcing to reduce costs, including laboratory services. Studies of this practice have largely focused on nonacademic medical centers. In contrast, academic medical centers have unique practice environments and unique mission considerations.