Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software

Transcriptomics-based identification of TYROBP and TLR8 as novel macrophage-related biomarkers for the diagnosis of acute rejection after kidney transplantation

Macrophages play an important role in the development and progression of acute rejection after kidney transplantation. The study aims to investigate the biological role and significance of macrophage-associated genes (MAG) in acute rejection after kidney transplantation. We utilized transcriptome sequencing results from public databases related to acute rejection of kidney transplantation for comprehensive analysis and validation in animal experiments. We found that a large number of immune-related signaling pathways are activated in acute rejection. PPI protein interaction networks and machine learning were used to establish a Hub gene consisting of TYROBP and TLR8 for the diagnosis of acute rejection. The single-gene GSEA enrichment analysis and immune cell correlation analysis revealed a close correlation between the expression of Hub genes and immune-related biological pathways as well as the expression of multiple immune cells. In addition, the study of TF, miRNAs, and drugs provided a theoretical basis for regulating and treating the Hub genes in acute rejection. Finally, the animal experiments demonstrated once again that acute rejection can aggravate kidney tissue damage, apoptosis level, and increase the release of inflammatory factors. We established and validated a macrophage-associated diagnostic model for acute rejection after kidney transplantation, which can accurately diagnose the biological alterations in acute rejection after kidney transplantation.

Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions

INTRODUCTION

Artificial intelligence (AI) integration in nephropathology has been growing rapidly in recent years, facing several challenges including the wide range of histological techniques used, the low occurrence of certain diseases, and the need for data sharing. This narrative review retraces the history of AI in nephropathology and provides insights into potential future developments.

METHODS

Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about automated image analysis or the application of an AI algorithm on non-neoplastic kidney histological samples were included and analyzed to extract information such as publication year, AI task, and learning type. Prepublication servers and reviews were not included.

RESULTS

Seventy-six (76) original research articles were selected. Most of the studies were conducted in the United States in the last 7 years. To date, research has been mainly conducted on relatively easy tasks, like single-stain glomerular segmentation. However, there is a trend towards developing more complex tasks such as glomerular multi-stain classification.

CONCLUSION

Deep learning has been used to identify patterns in complex histopathology data and looks promising for the comprehensive assessment of renal biopsy, through the use of multiple stains and virtual staining techniques. Hybrid and collaborative learning approaches have also been explored to utilize large amounts of unlabeled data. A diverse team of experts, including nephropathologists, computer scientists, and clinicians, is crucial for the development of AI systems for nephropathology. Collaborative efforts among multidisciplinary experts result in clinically relevant and effective AI tools.

Immunohistochemical expression of CD 14 in transitional cell carcinoma of the urinary bladder

CD14 is a co-receptor for bacterial lipolysaccharide (LPS) detection. It is found on myelomonocytic cells such as monocytes, macrophages, and Langerhans cells, CD14 expression in bladder cells is necessary for cytokine secretion and increased tumor growth. The goal of this study was to use immunohistochemistry (IHC) to assess CD14 expression in patients with transitional cell carcinoma of the urinary bladder in order to see if there was a link between CD14 marker expression in bladder cancer and cystitis. The immunoexpression of CD14 in paraffin sections from 30 bladder biopsy samples was separated into three groups: cystitis, low grade bladder cancer (L.G), and high grade bladder cancer (H.G), and studied using immunohistochemical assays (IHC). For bladder cancer (L.G & H.G), the percentage of samples that gave positive results for IHC/CD14 expression was 70% and 80%, respectively, compared to 30% for cystitis. The incidence of study samples appear in both sexes.

Digital Imaging Analysis Reveals Reduced Alveolar α-Smooth Muscle Actin Expression in Severe Asthma

Expansion of α-smooth muscle actin (α-SMA)-expressing airway smooth muscle of the large airways in asthma is well-studied. However, the contribution of α-SMA-expressing cells in the more distal alveolated parenchyma, including pericytes and myofibroblasts within the alveolar septum, to asthma pathophysiology remains relatively unexplored. The objective of this study was to evaluate α-SMA expression in the alveolated parenchyma of individuals with severe asthma (SA), compared with healthy controls or individuals with chronic obstructive pulmonary disease. Using quantitative digital image analysis and video-assisted thoracoscopic surgery lung biopsies, we show that alveolated parenchyma α-SMA expression is markedly reduced in SA in comparison to healthy controls (mean %positive pixels: 12% vs. 23%, P=0.005). Chronic obstructive pulmonary disease cases showed a similar, but trending, decrease in α-SMA positivity compared with controls (mean %positivity: 17% vs. 23%, P=0.107), which may suggest loss of α-SMA expression is a commonality of obstructive lung diseases. The SA group had similar staining for ETS-related gene protein, a specific endothelial marker, comparatively to controls (mean %positive nuclei: 34% vs. 42%, P=0.218), which suggests intact capillary endothelium and likely intact capillary-associated, α-SMA-positive pericytes. These findings suggest that the loss of α-SMA expression in SA may be because of changes in myofibroblast α-SMA expression or cell number. Further study is necessary to fully evaluate possible mechanisms and consequences of this phenomenon.

Image Analysis Pipeline for Renal Allograft Evaluation and Fibrosis Quantification

INTRODUCTION

Digital pathology improves the standardization and reproducibility of kidney biopsy specimen assessment. We developed a pipeline allowing the analysis of many images without requiring human preprocessing and illustrate its use with a simple algorithm for quantification of interstitial fibrosis on a large dataset of kidney allograft biopsy specimens.

METHODS

Masson trichrome-stained images from kidney allograft biopsy specimens were used to train and validate a glomeruli detection algorithm using a VGG19 convolutional neural network and an automatic cortical region of interest (ROI) selection algorithm including cortical regions containing all predicted glomeruli. A positive-pixel count algorithm was used to quantify interstitial fibrosis on the ROIs and the association between automatic fibrosis and pathologist evaluation, estimated glomerular filtration rate (GFR) and allograft survival was assessed.

RESULTS

The glomeruli detection (F1 score of 0.87) and ROIs selection (F1 score 0.83 [SD 0.13]) algorithms displayed high accuracy. The correlation between the automatic fibrosis quantification on manually and automatically selected ROIs was high (r = 1.00 [0.99-1.00]). Automatic fibrosis quantification was only moderately correlated with pathologists' assessment and was not significantly associated with eGFR or allograft survival.

CONCLUSION

This pipeline can automatically and accurately detect glomeruli and select cortical ROIs that can easily be used to develop, validate, and apply image analysis algorithms.

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.

Banff Digital Pathology Working Group: Going digital in transplant pathology

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made.

Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring

Traditional histopathological allograft biopsy evaluation provides, within hours, diagnoses, prognostic information, and mechanistic insights into disease processes. However, proponents of an array of alternative monitoring platforms, broadly classified as "invasive" or "noninvasive" depending on whether allograft tissue is needed, question the value proposition of tissue histopathology. The authors explore the pros and cons of current analytical methods relative to the value of traditional and illustrate advancements of next-generation histopathological evaluation of tissue biopsies. We describe the continuing value of traditional histopathological tissue assessment and "next-generation pathology (NGP)," broadly defined as staining/labeling techniques coupled with digital imaging and automated image analysis. Noninvasive imaging and fluid (blood and urine) analyses promote low-risk, global organ assessment, and "molecular" data output, respectively; invasive alternatives promote objective, "mechanistic" insights by creating gene lists with variably increased/decreased expression compared with steady state/baseline. Proponents of alternative approaches contrast their preferred methods with traditional histopathology and: (1) fail to cite the main value of traditional and NGP-retention of spatial and inferred temporal context available for innumerable objective analyses and (2) belie an unfamiliarity with the impact of advances in imaging and software-guided analytics on emerging histopathology practices. Illustrative NGP examples demonstrate the value of multidimensional data that preserve tissue-based spatial and temporal contexts. We outline a path forward for clinical NGP implementation where "software-assisted sign-out" will enable pathologists to conduct objective analyses that can be incorporated into their final reports and improve patient care.

Histological features of skin and subcutaneous tissue in patients with breast cancer who have received neoadjuvant chemotherapy and their relationship to post-treatment edema

BACKGROUND

Lymphedema is a major complication of treatment for breast cancer. Although chemotherapy can cause lymphedema, there have been few reports about histological changes in skin and subcutaneous tissue after chemotherapy. The aim of our study was to determine whether chemotherapy affects blood and lymphatic vessels in the skin and subcutaneous fat and to investigate the relationship between these changes and extent of post-chemotherapy edema.

METHODS

We compared histological findings in skin and subcutaneous fat of mastectomy specimens from 38 patients who had received NAC (neoadjuvant chemotherapy) and 56 who had not (non-NAC) attending our institution from 2007 to 2016. Patients whose tumor may have affected the area examined were excluded. Blood and lymphatic vessels were identified by CD31 and D2-40, respectively. We assessed microvessel density (MVD), lymphatic microvessel density (MLVD), lumen cross-sectional area (LA), and amount of endothelium (AE) in blood and lymphatic vessels. To minimize surgical effects, we measured edema, defined as ≥ 15% thicker dorsal subcutaneous tissue than baseline, on the contralateral side.

RESULTS

MVD, LA, and AE of blood vessels were greater and MLVD not significantly different in the skin of NAC patients than in that of non-NAC patients. MVD was greater and AE of blood vessels less in subcutaneous fat of NAC patients than in that of non-NAC patients. Patients with edema had significantly less AE of blood vessels in skin than did those without it.

CONCLUSIONS

These pathological findings can help to identify patients who will develop edema and improve their treatment.

The Landscape of Digital Pathology in Transplantation: From the Beginning to the Virtual E-Slide

Background

Digital pathology has progressed over the last two decades, with many clinical and nonclinical applications. Transplantation pathology is a highly specialized field in which the majority of practicing pathologists do not have sufficient expertise to handle critical needs. In this context, digital pathology has proven to be useful as it allows for timely access to expert second-opinion teleconsultation. The aim of this study was to review the experience of the application of digital pathology to the field of transplantation.

Methods

Papers on this topic were retrieved using PubMed as a search engine. Inclusion criteria were the presence of transplantation setting and the use of any type of digital image with or without the use of image analysis tools; the search was restricted to English language papers published in the 25 years until December 31, 2018.

Results

Literature regarding digital transplant pathology is mostly about the digital interpretation of posttransplant biopsies (75 vs. 19), with 15/75 (20%) articles focusing on agreement/reproducibility. Several papers concentrated on the correlation between biopsy features assessed by digital image analysis (DIA) and clinical outcome (45/75, 60%). Whole-slide imaging (WSI) only appeared in recent publications, starting from 2011 (13/75, 17.3%). Papers dealing with preimplantation biopsy are less numerous, the majority (13/19, 68.4%) of which focus on diagnostic agreement between digital microscopy and light microscopy (LM), with WSI technology being used in only a small quota of papers (4/19, 21.1%).

Conclusions

Overall, published studies show good concordance between digital microscopy and LM modalities for diagnosis. DIA has the potential to increase diagnostic reproducibility and facilitate the identification and quantification of histological parameters. Thus, with advancing technology such as faster scanning times, better image resolution, and novel image algorithms, it is likely that WSI will eventually replace LM.