Semi-Automatic Classification Of Histopathological Images: Dealing With Inter-Slide Variations

Introduction/ Background The large size and high resolution of histopathological whole slide images renders their manual annotation time-consuming and costly. State-of-the-art computer-based segmentation approaches are generally able to classify tissue reliably, but strong inter-slide variations between training and evaluation data can cause significant decreases in classification accuracy. Aims In this study, we focus on alpha-SMA stainings of the mouse kidney, and in particular on the classification of glomerular vs. non-glomerular regions. Even though all slides had been recorded using a common staining protocol, inter-slide variations could be observed. We investigate the impact of these variations as well as methods of resolution. Methods We propose an interactive, semi-automatic tissue classification approach [1] which adapts a pre-trained classification model to the new image on which classification should be performed. Image patches for which the class (glomerular/non-glomerular) is uncertain are automatically selected and presented to the user to determine the class label. The user interaction step is repeated several times to iteratively adjust the model to the characteristics of the new image. For image representation and classification, well known methods from the literature are utilized. Specifically, we combine Local Binary Patters with the support vector classifier. Results In case of 50 available labelled sample patches of a certain whole slide image, the overall classification rate increased from 92 % to 98 % through including the interactive labelling step. Even with only 20 labelled patches, accuracy already increased to 97 %. Without a pre-trained model, if training is performed on target domain data only, 88 % (20 labelled samples) and 95 % (50 labelled samples) accuracy, respectively, were obtained. If enough target domain data was available (about 20 images), the amount of source domain data was of minor relevance. The difference in outcome between a source domain training data set containing 100 patches from one whole slide image and a set containing 700 patches from seven images was lower than 1 %. Contrarily, without target domain data, the difference in accuracy was 10 % (82 % compared to 92 %) between these two settings. Execution runtime between two interaction steps is significantly below one second (0.23 s), which is an important usability criterion. It proved to be beneficial to select specific target domain data in an active learning sense based on the currently available trained model. While experimental evaluation provided strong empirical evidence for increased classification performance with the proposed method, the additional manual effort can be kept at a low level. The labelling of e.g. 20 images per slide is surely less time consuming than the validation of a complete whole slide image processed with a fully automatic, but less reliable, segmentation approach. Finally, it should be highlighted that the proposed interaction protocol could easily be adapted to other histopathological classification or segmentation tasks, also for implementation in a clinical system.

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study

Background Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection.Methods We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance).Findings Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0.87 [ten times bootstrapped CI 0.85-0.88]) and disease (0.87 [0.86-0.88]), followed by a second CNN classifying biopsies classified as disease into rejection (0.75 [0.73-0.76]) and other diseases (0.75 [0.72-0.77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0.83 [0.80-0.85], disease 0.83 [0.73-0.91]; second CNN rejection 0.61 [0.51-0.70], other diseases 0.61 [0.50-4.74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0.80 [0.73-0.84], rejection 0.76 [0.66-0.80], other diseases 0.50 [0.36-0.57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium.Interpretation This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Immunopathology of vascular and renal diseases and of organ and celltransplantationIP

Pathology and natural history of organ fibrosis

Histopathological assessment of fibrosis focusing on morphological patterns provides important information for the management of patients with chronic diseases of the kidney, liver and the lung. This review summarizes key histopathological features of pulmonary, renal and hepatic fibrosis and discusses advances in the understanding of the pathogenesis of pulmonary fibrosis and pathogenetic insights with translational implications for renal fibrosis. The review also tackles new staging approaches based on liver fibrosis dynamics and evaluation of fibrosis regression, digital pathology and second harmonic generation microscopy methods for hepatic fibrosis assessment and critical appraisal of non-invasive tests for liver and renal fibrosis evaluation. © 2019 Elsevier Lt

Non-invasive molecular imaging of kidney diseases

In nephrology, differential diagnosis or assessment of disease activity largely relies on the analysis of glomerular filtration rate, urinary sediment, proteinuria and tissue obtained through invasive kidney biopsies. However, currently available non-invasive functional parameters, and most serum and urine biomarkers cannot capture intrarenal molecular disease processes specifically. Moreover, although histopathological analyses of kidney biopsy samples enable the visualization of pathological morphological and molecular alterations, they only provide information on a small part of the kidney and do not allow longitudinal monitoring. These limitations not only hinder understanding of the dynamics of specific disease processes in the kidney, but also limit the targeting of treatments to active phases of disease and the development of novel targeted therapies. Molecular imaging enables non-invasive and quantitative assessment of physiological or pathological processes by combining imaging technologies with specific molecular probes. Here, we discuss current preclinical and clinical molecular imaging approaches in nephrology. Molecular imaging enables non-invasive visualization of the kidneys, and helps to detect and longitudinally monitor disease activity. These approaches can also provide companion diagnostics to guide clinical trials, as well as the safe and effective use of drugs

Gesetzliche Einführung des Neugeborenen-Hörscreenings in die "Kinder-Richtlinien" im Januar 2009 - Ergebnisse und Erfahrungen aus dem HELIOS Klinikum Berlin-Buch

Progressive kidney diseases and renal fibrosis are associated with endothelial injury and capillary rarefaction. However, our understanding of these processes has been hampered by the lack of tools enabling the quantitative and noninvasive monitoring of vessel functionality. Here, we used micro-computed tomography (µCT) for anatomical and functional imaging of vascular alterations in three murine models with distinct mechanisms of progressive kidney injury: ischemia-reperfusion (I/R, days 1–56), unilateral ureteral obstruction (UUO, days 1–10), and Alport mice (6–8 weeks old). Contrast-enhanced in vivo µCT enabled robust, noninvasive, and longitudinal monitoring of vessel functionality and revealed a progressive decline of the renal relative blood volume in all models. This reduction ranged from −20% in early disease stages to −61% in late disease stages and preceded fibrosis. Upon Microfil perfusion, high-resolution ex vivo µCT allowed quantitative analyses of three-dimensional vascular networks in all three models. These analyses revealed significant and previously unrecognized alterations of preglomerular arteries: a reduction in vessel diameter, a prominent reduction in vessel branching, and increased vessel tortuosity. In summary, using µCT methodology, we revealed insights into macro-to-microvascular alterations in progressive renal disease and provide a platform that may serve as the basis to evaluate vascular therapeutics in renal disease

A collagen-binding protein enables molecular imaging of kidney fibrosis in vivo

Pathological deposition of collagen is a hallmark of kidney fibrosis. To illustrate this process we employed multimodal optical imaging to visualize and quantify collagen deposition in murine models of kidney fibrosis (ischemia-reperfusion or unilateral ureteral obstruction) using the collagen-binding adhesion protein CNA35. For in vivo imaging, we used hybrid computed tomography-fluorescence molecular tomography and CNA35 labeled with the near-infrared fluorophore Cy7. Upon intravenous injection, CNA35-Cy7 accumulation was significantly higher in fibrotic compared to non-fibrotic kidneys. This difference was not detected for a non-specific scrambled version of CNA35-Cy7. Ex vivo, on kidney sections of mice and patients with renal fibrosis, CNA35-FITC co-localized with fibrotic collagen type I and III, but not with the basement membrane collagen type IV. Following intravenous injection, CNA35-FITC bound to both interstitial and perivascular fibrotic areas. In line with this perivascular accumulation, we observed significant perivascular fibrosis in the mouse models and in biopsy sections from patients with chronic kidney disease using computer-based morphometry quantification. Thus, molecular imaging of collagen using CNA35 enabled specific non-invasive quantification of kidney fibrosis. Collagen imaging revealed significant perivascular fibrosis as a consistent component next to the more commonly assessed interstitial fibrosis. Our results lay the basis for further probe and protocol optimization towards the clinical translation of molecular imaging of kidney fibrosis

Empagliflozin improves left ventricular diastolic function of db/db mice.

Objectives: Investigation of the effect of SGLT2 inhibition by empagliflozin on left ventricular function in a model of diabetic cardiomyopathy.Background: SGLT2 inhibition is a new strategy to treat diabetes. In the EMPA-REG Outcome trial empagliflozin treatment reduced cardiovascular and overall mortality in patients with diabetes presumably due to beneficial cardiac effects, leading to reduced heart failure hospitalization. The relevant mechanisms remain currently elusive but might be mediated by a shift in cardiac substrate utilization leading to improved energetic supply to the heart.Methods: We used db/db mice on high-fat western diet with or without empagliflozin treatment as a model of severe diabetes. Left ventricular function was assessed by pressure catheter with or without dobutamine stress.Results: Treatment with empagliflozin significantly increased glycosuria, improved glucose metabolism, ameliorated left ventricular diastolic function and reduced mortality of mice. This was associated with reduced cardiac glucose concentrations and decreased calcium/calmodulin-dependent protein kinase (CaMKII) activation with subsequent less phosphorylation of the ryanodine receptor (RyR). No change of cardiac ketone bodies or branched-chain amino acid (BCAA) metabolites in serum was detected nor was cardiac expression of relevant catabolic enzymes for these substrates affected.Conclusions: In a murine model of severe diabetes empagliflozin-dependent SGLT2 inhibition improved diastolic function and reduced mortality. Improvement of diastolic function was likely mediated by reduced spontaneous diastolic sarcoplasmic reticulum (SR) calcium release but independent of changes in cardiac ketone and BCAA metabolism

Pro-cachectic factors link experimental and human chronic kidney disease to skeletal muscle wasting programs

cited By 1Skeletal muscle wasting is commonly associated with chronic kidney disease (CKD), resulting in increased morbidity and mortality. However, the link between kidney and muscle function remains poorly understood. Here, we took a complementary interorgan approach to investigate skeletal muscle wasting in CKD. We identified increased production and elevated blood levels of soluble pro-cachectic factors, including activin A, directly linking experimental and human CKD to skeletal muscle wasting programs. Single-cell sequencing data identified the expression of activin A in specific kidney cell populations of fibroblasts and cells of the juxtaglomerular apparatus. We propose that persistent and increased kidney production of procachectic factors, combined with a lack of kidney clearance, facilitates a vicious kidney/muscle signaling cycle, leading to exacerbated blood accumulation and, thereby, skeletal muscle wasting. Systemic pharmacological blockade of activin A using soluble activin receptor type IIB ligand trap as well as muscle-specific adeno-associated virus-mediated downregulation of its receptor ACVR2A/B prevented muscle wasting in different mouse models of experimental CKD, suggesting that activin A is a key factor in CKD-induced cachexia. In summary, we uncovered a crosstalk between kidney and muscle and propose modulation of activin signaling as a potential therapeutic strategy for skeletal muscle wasting in CKD.Peer reviewe