Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study.

Aims: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples.

Methods: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings.

Technical note on the exploration of COVID-19 in autopsy material.

Interrogation of immune response in autopsy material from patients with SARS-CoV-2 is potentially significant. We aim to describe a validated protocol for the exploration of the molecular physiopathology of SARS-CoV-2 pulmonary disease using multiplex immunofluorescence (mIF).The application of validated assays for the detection of SARS-CoV-2 in tissues, originally developed in our laboratory in the context of oncology, was used to map the topography and complexity of the adaptive immune response at protein and mRNA levels.

Using Systemised Nomenclature of Medicine (SNOMED) codes to select digital pathology whole slide images for long-term archiving.

The archiving of whole slide images represents a hurdle to digital pathology implementation largely because of the amount of data generated. The retention of glass slides is currently recommended for a minimum of 10 years, but it is for individual departments to determine how digital images are archived and for how long. In a retrospective study, we examined the combination of Systemised Nomenclature of Medicine (SNOMED) codes allocated to cases reported between July 2011 and December 2015 and recalled more than 12 months after diagnosis in comparison to non-recalled cases.

Lessons from a breast cell annotation competition series for school pupils.

Due to COVID-19 outbreaks, most school pupils have had to be home-schooled for long periods of time. Two editions of a web-based competition "Beat the Pathologists" for school age participants in the UK ran to fill up pupils' spare time after home-schooling and evaluate their ability on contributing to AI annotation. The two editions asked the participants to annotate different types of cells on Ki67 stained breast cancer images.

Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies.

The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request.

Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis.

Background: Digital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence-based computer-assisted diagnostics. Although the barriers to wider adoption of DP have been multifactorial, limited evidence of reliability has been a significant contributor. A meta-analysis to demonstrate the combined accuracy and reliability of DP is still lacking in the literature.

Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective.

The measures to control the COVID-19 outbreak will likely remain a feature of our working lives until a suitable vaccine or treatment is found. The pandemic has had a substantial impact on clinical services, including cancer pathways. Pathologists are working remotely in many circumstances to protect themselves, colleagues, family members and the delivery of clinical services.

Interobserver Reliability of Programmed Cell Death Ligand-1 Scoring Using the VENTANA PD-L1 (SP263) Assay in NSCLC.

Introduction: The VENTANA PD-L1 (SP263) Assay is approved for use with anti-programmed cell death-1/programmed cell death ligand-1 (PD-1/PD-L1) therapies in NSCLC and urothelial carcinoma. Here, we investigate interobserver reliability of the SP263 assay, applied to PD-L1 scoring of tumor cells (TCs) in NSCLC.

Methods: Six practicing European pulmonary pathologists independently scored the proportion of TCs expressing PD-L1 (TC score) from 200 archival, commercially sourced, formalin-fixed paraffin-embedded NSCLC resections stained using the SP263 assay.

Altered Decorin Biology in Proliferative Vitreoretinopathy: A Mechanistic and Cohort Study.

Purpose: To determine if vitreous levels of the pro-fibrotic cytokine transforming growth factor beta2 (TGF-β2) and its opposing regulator decorin predict subsequent proliferative vitreoretinopathy (PVR) development in patients with rhegmatogenous retinal detachment (RRD).

Methods: We examined the effect of TGF-β2 and decorin on epithelial-mesenchymal transition (EMT) and collagen expression in vitro using ARPE-19 cells, and we analyzed extracellular matrix marker expression in PVR membrane and internal limiting membrane patient samples. We performed a prospective noninterventional cohort study, recruiting 125 patients undergoing vitrectomy for RRD and macular hole surgery, measured vitreous levels of TGF-β2 and decorin by ELISA, and followed them up for 6 months.

Gland segmentation in colon histology images: The glas challenge contest.

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology.

Medical Students' Perceptions of Clinical Teachers as Role Model.

Introduction: Role models facilitate student learning and assists in the development of professional identity. However, social organization and cultural values influence the choice of role models. Considering that the social organization and cultural values in South East Asia are different from other countries, it is important to know whether this affects the characteristics medical students look for in their role models in these societies.

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images.

Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection.

Validation of digital pathology imaging for primary histopathological diagnosis.

Aims: Digital pathology (DP) offers advantages over glass slide microscopy (GS), but data demonstrating a statistically valid equivalent (i.e. non-inferior) performance of DP against GS are required to permit its use in diagnosis.

A Stochastic Polygons Model for Glandular Structures in Colon Histology Images.

In this paper, we present a stochastic model for glandular structures in histology images of tissue slides stained with Hematoxylin and Eosin, choosing colon tissue as an example. The proposed Random Polygons Model (RPM) treats each glandular structure in an image as a polygon made of a random number of vertices, where the vertices represent approximate locations of epithelial nuclei. We formulate the RPM as a Bayesian inference problem by defining a prior for spatial connectivity and arrangement of neighboring epithelial nuclei and a likelihood for the presence of a glandular structure.

Caspase-9 mediates photoreceptor death after blunt ocular trauma.

Purpose: Ocular trauma is common in civilian and military populations. Commotio retinae involves acute disruption of photoreceptor outer segments after blunt ocular trauma, with subsequent photoreceptor apoptosis causing permanent visual impairment. The mechanisms of photoreceptor death in commotio retinae have not previously been described, although caspase-dependent death is important in other nontraumatic retinal degenerations.

Elevated soluble CD163 in gestational diabetes mellitus: secretion from human placenta and adipose tissue.

Recently soluble CD163 (sCD163), a cleaved form of the macrophage receptor CD163, was identified as a macrophage-specific risk-predictor for developing Type 2 Diabetes. Here, we investigate circulating levels of sCD163 in gestational diabetes mellitus (GDM). Furthermore, given the role of the placenta in the pathogenesis of GDM, we assessed placental contribution to sCD163 secretion.

BAI3, CDX2 and VIL1: a panel of three antibodies to distinguish small cell from large cell neuroendocrine lung carcinomas.

Aims: Discriminating small-cell lung carcinoma (SCLC) from large-cell neuroendocrine carcinoma (LCNEC) rests on morphological criteria, and reproducibility has been shown to be poor. We aimed to identify immunohistochemical markers to assist this diagnosis.

Methods And Results: Gene expression profiling on laser captured frozen tumour samples from eight SCLC and eight LCNEC tumours identified a total of 888 differentially expressed genes (DEGs), 23 of which were validated by qRT-PCR.

Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics.

Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm.

Neuroretinal cell death in a murine model of closed globe injury: pathological and functional characterization.

Purpose: Blunt ocular trauma causes severe retinal injury with death of neuroretinal tissue, scarring, and permanent visual loss. The mechanisms of cell death are not known, and there are no therapeutic interventions that improve visual outcome. We aimed to study the extent, distribution, and functional consequences of cell death by developing and characterizing a rat model of retinal injury caused by blunt ocular trauma.

Differences in amyloid deposition in primary and recurrent corneal lattice dystrophy type 1.

Purpose: To report the histopathology of a case of recurrent corneal lattice dystrophy showing altered distribution of the corneal deposits in the recurrent disease compared with the original.

Methods: Clinical details and histopathology of the primary and repeat corneal grafts are reported.

Results: A woman originally presented at age 28 years with reduced visual acuity and classic corneal lattice lines in both corneas and underwent bilateral corneal grafts.