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Background:\ud Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as an alternative to current tests.\ud Methods:\ud In this retrospective study, we used 502 diagnostic slides of primary colorectal tumours from 499 patients in The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) cohort and developed a weakly supervised deep learning framework involving three separate convolutional neural network models. Whole-slide images were divided into equally sized tiles and model 1 (ResNet18) extracted tumour tiles from non-tumour tiles. These tumour tiles were inputted into model 2 (adapted ResNet34), trained by iterative draw and rank sampling to calculate a prediction score for each tile that represented the likelihood of a tile belonging to the molecular labels of high mutation density (vs low mutation density), microsatellite instability (vs microsatellite stability), chromosomal instability (vs genomic stability), CpG island methylator phenotype (CIMP)-high (vs CIMP-low), BRAFmut (vs BRAFWT), TP53mut (vs TP53WT), and KRASWT (vs KRASmut). These scores were used to identify the top-ranked titles from each slide, and model 3 (HoVer-Net) segmented and classified the different types of cell nuclei in these tiles. We calculated the area under the convex hull of the receiver operating characteristic curve (AUROC) as a model performance measure and compared our results with those of previously published methods.\ud Findings:\ud Our iterative draw and rank sampling method yielded mean AUROCs for the prediction of hypermutation (0·81 [SD 0·03] vs 0·71), microsatellite instability (0·86 [0·04] vs 0·74), chromosomal instability (0·83 [0·02] vs 0·73), BRAFmut (0·79 [0·01] vs 0·66), and TP53mut (0·73 [0·02] vs 0·64) in the TCGA-CRC-DX cohort that were higher than those from previously published methods, and an AUROC for KRASmut that was similar to previously reported methods (0·60 [SD 0·04] vs 0·60). Mean AUROC for predicting CIMP-high status was 0·79 (SD 0·05). We found high proportions of tumour-infiltrating lymphocytes and necrotic tumour cells to be associated with microsatellite instability, and high proportions of tumour-infiltrating lymphocytes and a low proportion of necrotic tumour cells to be associated with hypermutation.\ud Interpretation:\ud After large-scale validation, our proposed algorithm for predicting clinically important mutations and molecular pathways, such as microsatellite instability, in colorectal cancer could be used to stratify patients for targeted therapies with potentially lower costs and quicker turnaround times than sequencing-based or immunohistochemistry-based approaches.\ud Funding:\ud The UK Medical Research Council.

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. The effects of COVID-19 on research and clinical trials have also been significant with changes to protocols, suspensions of studies and redeployment of resources to COVID-19. In this article, we explore the specific impact of COVID-19 on clinical and academic pathology and explore how digital pathology and artificial intelligence can play a key role to safeguarding clinical services and pathology-based research in the current climate and in the future.

Aim The rate of deployment of digital pathology (DP) systems for primary diagnosis in the UK is accelerating. The flexibility and resilience of digital versus standard glass slides could be of great benefit in the NHS breast screening programme (NHSBSP). This study aims to document the safety and benefits of DP for preoperative tissue diagnosis of screen-detected breast lesions. Methods and results Concordance data for glass and digital slides of the same cases from four sites were subjected to detailed concordance-discordance analysis. A literature review of DP in the primary diagnosis of breast lesions is presented, making this the most comprehensive synthesis of digital breast cancer histopathological diagnostic data to date. Detailed concordance analysis of experimental data from two histopathology departments reveals clinical concordance rates for breast biopsies of 96% (216 of 225) and 99.6% (249 of 250). Data from direct comparison validation studies in two histopathology departments, utilising the protocol recommended by the Royal College of Pathologists, found concordance rates for breast histology cases of 99.4% (180 of 181) and 99.0% (887 of 896). An intraobserver variation study for glass versus digital slides for difficult cases from the NHSBSP yielded a kappa statistic of 0.80, indicating excellent agreement. Discordances encountered in the studies most frequently concerned discrepancies in grading attributable to mitotic count-scoring and identification of weddelite. Conclusions The experience of four histopathology laboratories and our review of pre-existing literature suggests that DP is safe for the primary diagnosis of NHSBSP breast histology specimens, and does not increase the risk of misclassification.

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. Agreement in scores was analyzed using the intraclass correlation coefficient and concordance in patient's classification using Fleiss’ kappa. Results: Results from 172 samples showed strong pair-wise correlations between pathologists (R2 >0.89) for TC scoring with an intraclass correlation coefficient of 0.96. Overall agreement was greater than 90% for TC of 1% and above, and greater than 94% for TCs of at least 25% and at least 50%. Fleiss’ kappa showed substantial agreement for TC of 1% and above, and almost perfect agreement for TCs of at least 25% and at least 50%. Conclusions: Assessment of TC score in NSCLC was highly reproducible using the SP263 assay, building confidence in the accuracy of this assay in selection of patients for anti–PD-1/PD-L1 therapy.

There are very few published cases of total anterior staphyloma, all of which have been reported as secondary to fungal keratitis. This study reports the clinical and histopathological findings and subsequent management of a 27-year-old healthy female patient who developed total anterior staphyloma after poor compliance with treatment for clinically diagnosed acanthamoeba keratitis. She underwent a successful evisceration with good long-term results. This case highlights that total anterior staphyloma may also result from untreated keratitis which is not fungal in origin. In cases of fungal and acanthamoeba keratitis, patient compliance with both treatment and follow-up is paramount to avoid vision-threatening sequelae that present significant challenges in their management.

The development of deep segmentation models for computational pathology (CPath) can help foster the investigation of interpretable morphological biomarkers. Yet, there is a major bottleneck in the success of such approaches because supervised deep learning models require an abundance of accurately labelled data. This issue is exacerbated in the field of CPath because the generation of detailed annotations usually demands the input of a pathologist to be able to distinguish between different tissue constructs and nuclei. Manually labelling nuclei may not be a feasible approach for collecting large-scale annotated datasets, especially when a single image region can contain thousands of different cells. However, solely relying on automatic generation of annotations will limit the accuracy and reliability of ground truth. Therefore, to help overcome the above challenges, we propose a multi-stage annotation pipeline to enable the collection of large-scale datasets for histology image analysis, with pathologist-in-the-loop refinement steps. Using this pipeline, we generate the largest known nuclear instance segmentation and classification dataset, containing nearly half a million labelled nuclei in H&E stained colon tissue. We have released the dataset and encourage the research community to utilise it to drive forward the development of downstream cell-based models in CPath.

We present an asthmatic 39 year old female frontline healthcare worker in contact with COVID-19 patients, who suffered from respiratory problems for a few days, after which she was found dead by her spouse. Post-mortem (PM) examination showed lung consolidation and histological evidence of diffuse alveolar damage (DAD), in form of hyaline membrane disease, as well as atypical cells, thrombi and fibrin plugs inside pulmonary capillaries. Swab sample testing for SARS-CoV-2 confirmed the infection status. SARS-CoV-2 is a novel coronavirus which first emerged in Wuhan, China, and PM is contributing immensely to uncovering the pathophysiological mechanism of this disease. SARS-CoV-2 enters host cells via the angiotensin-converting enzyme 2 (ACE2), and can lead to a fatal pneumonia characterised histologically by DAD (the most consistent PM finding). This is due to invasion by the virus of type II pneumocytes, which leads to the death of both type I and II pneumocytes and increased capillary permeability. This compromises gas exchange which can lead eventually to cardiorespiratory failure and death.

BackgroundDigital 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.ObjectivesWe aimed to review the published literature on the diagnostic use of DP and to synthesise a statistically pooled evidence on safety and reliability of DP for routine diagnosis (primary and secondary) in the context of validation process.MethodsA comprehensive literature search was conducted through PubMed, Medline, EMBASE, Cochrane Library and Google Scholar for studies published between 2013 and August 2019. The search protocol identified all studies comparing DP with light microscopy (LM) reporting for diagnostic purposes, predominantly including H&E-stained slides. Random-effects meta-analysis was used to pool evidence from the studies.ResultsTwenty-five studies were deemed eligible to be included in the review which examined a total of 10 410 histology samples (average sample size 176). For overall concordance (clinical concordance), the agreement percentage was 98.3% (95% CI 97.4 to 98.9) across 24 studies. A total of 546 major discordances were reported across 25 studies. Over half (57%) of these were related to assessment of nuclear atypia, grading of dysplasia and malignancy. These were followed by challenging diagnoses (26%) and identification of small objects (16%).ConclusionThe results of this meta-analysis indicate equivalent performance of DP in comparison with LM for routine diagnosis. Furthermore, the results provide valuable information concerning the areas of diagnostic discrepancy which may warrant particular attention in the transition to DP.

Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironment. Most existing methods for tissue phenotyping are limited to the classification of tumor and stroma and require large amount of annotated histology images which are often not available. In the current work, we pose the problem of identifying distinct tissue phenotypes as finding communities in cellular graphs or networks. First, we train a deep neural network for cell detection and classification into five distinct cellular components. Considering the detected nuclei as nodes, potential cell-cell connections are assigned using Delaunay triangulation resulting in a cell-level graph. Based on this cell graph, a feature vector capturing potential cell-cell connection of different types of cells is computed. These feature vectors are used to construct a patch-level graph based on chi-square distance. We map patch-level nodes to the geometric space by representing each node as a vector of geodesic distances from other nodes in the network and iteratively drifting the patch nodes in the direction of positive density gradients towards maximum density regions. The proposed algorithm is evaluated on a publicly available dataset and another new large-scale dataset consisting of 280K patches of seven tissue phenotypes. The estimated communities have significant biological meanings as verified by the expert pathologists. A comparison with current state-of-the-art methods reveals significant performance improvement in tissue phenotyping.\ud \ud

Pathology departments must rise to new staffing challenges caused by the coronavirus disease-19 pandemic and may need to work more flexibly for the foreseeable future. In light of this, many pathologists and departments are considering the merits of remote or home reporting of digital cases. While some individuals have experience of this, little work has been done to determine optimum conditions for home reporting, including technical and training considerations. In this publication produced in response to the pandemic, we provide information regarding risk assessment of home reporting of digital slides, summarize available information on specifications for home reporting computing equipment, and share access to a novel point-of-use quality assurance tool for assessing the suitability of home reporting screens for digital slide diagnosis. We hope this study provides a useful starting point and some practical guidance in a difficult time. This study forms the basis of the guidance issued by the Royal College of Pathologists, available at: https://www.rcpath.org/uploads/assets/626ead77-d7dd-42e1-949988e43dc84c97/RCPath-guidance-for-remote-digital-pathology.pdf.

Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. $224\times 224$ ) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate a larger context by a context-aware neural network based on images with a dimension of $1792\times 1792$ pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. We evaluated the proposed method on two colorectal cancer datasets for the task of cancer grading. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods. We also presented a comprehensive analysis of different variants of the proposed method.

Pemphigus vulgaris (PV) is a rare autoimmune bullous disease which affects the skin and mucous membranes. Oesophageal involvement is rare and has previously been limited to case reports and case series. A recent large case series of 477 PV patients showed that 26/477 (5.4%) had symptomatic oesophageal involvement. We present the case of a 54-year-old Somalian lady with a 10-year history of cutaneous PV, currently in remission, who developed dysphagia and odynophagia and was subsequently found to have oesophageal PV involvement with multiple flaccid bullae which were positive for anti-DSG3 antibodies on in-direct immunofluorescence. She had her treatment switched from azathioprine to mycophenolate and prednisolone, leading to resolution of her symptoms.

Determining the grade of colon cancer from tissue slides is a routine part of the pathological analysis. In the case of colorectal adenocarcinoma (CRA), grading is partly determined by morphology and degree of formation of glandular structures. Achieving consistency between pathologists is difficult due to the subjective nature of grading assessment. An objective grading using computer algorithms will be more consistent, and will be able to analyse images in more detail. In this paper, we measure the shape of glands with a novel metric that we call the Best Alignment Metric (BAM). We show a strong correlation between a novel measure of glandular shape and grade of the tumour. We used shape specific parameters to perform a two-class classification of images into normal or cancerous tissue and a three-class classification into normal, low grade cancer, and high grade cancer. The task of detecting gland boundaries, which is a prerequisite of shape-based analysis, was carried out using a deep convolutional neural network designed for segmentation of glandular structures. A support vector machine (SVM) classifier was trained using shape features derived from BAM. Through cross-validation, we achieved an accuracy of 97% for the two-class and 91% for three-class classification. 1 2017 The Author(s). This work was made possible by NPRP grant number NPRP5-1345-1-228 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The authors are grateful to Dr. Yee-Wah Tsang from the Department of Pathology, UHCW for her assistance in annotating gland boundaries. Scopus

Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co-localized scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images. Regions of tumor in a series of fifty consecutive breast core biopsies were identified by annotation on H&E whole slide images. Sequentially cut immunohistochemical stained sections were scored manually, before being digitally scanned and then exported into JPEG 2000 format. A two-stage registration process was performed to identify the annotated regions of interest in the immunohistochemistry sections, which were then scored using the Allred system. Overall correlation between manual and automated scoring for ER and PR was 0.944 and 0.883, respectively, with 90% of ER and 80% of PR scores within in one point or less of agreement. This proof of principle study indicates slide registration can be used as a basis for automation of the downstream analysis for clinically relevant biomarkers in the majority of cases. The approach is likely to be improved by implantation of safeguarding analysis steps post registration. © 2016 International Society for Advancement of Cytometry.

The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectivity among pathologists. The rise of computational pathology has led to the development of automated methods for gland segmentation that aim to overcome the challenges of manual segmentation. However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures. Furthermore, a measure of uncertainty is essential for diagnostic decision making. To address these challenges, we propose a fully convolutional neural network that counters the loss of information caused by max-pooling by re-introducing the original image at multiple points within the network. We also use atrous spatial pyramid pooling with varying dilation rates for preserving the resolution and multi-level aggregation. To incorporate uncertainty, we introduce random transformations during test time for an enhanced segmentation result that simultaneously generates an uncertainty map, highlighting areas of ambiguity. We show that this map can be used to define a metric for disregarding predictions with high uncertainty. The proposed network achieves state-of-the-art performance on the GlaS challenge dataset and on a second independent colorectal adenocarcinoma dataset. In addition, we perform gland instance segmentation on whole-slide images from two further datasets to highlight the generalisability of our method. As an extension, we introduce MILD-Net+ for simultaneous gland and lumen segmentation, to increase the diagnostic power of the network., Initial version published at Medical Imaging with Deep Learning (MIDL) 2018

Distant metastasis is the major cause of death in colorectal cancer (CRC). Patients at high risk of developing distant metastasis could benefit from appropriate adjuvant and follow-up treatments if stratified accurately at an early stage of the disease. Studies have increasingly recognized the role of diverse cellular components within the tumor microenvironment in the development and progression of CRC tumors. In this paper, we show that automated analysis of digitized images from locally advanced colorectal cancer tissue slides can provide estimate of risk of distant metastasis on the basis of novel tissue phenotypic signatures of the tumor microenvironment. Specifically, we determine what cell types are found in the vicinity of other cell types, and in what numbers, rather than concentrating exclusively on the cancerous cells. We then extract novel tissue phenotypic signatures using statistical measurements about tissue composition. Such signatures can underpin clinical decisions about the advisability of various types of adjuvant therapy.

Gastric cancer (GC) is the fourth commonest cancer worldwide, with the second highest mortality rate. Its poor mortality is linked to delayed presentation. There is a drive towards non-invasive biomarker screening and monitoring of many different types of cancer, although with limited success so far. We aimed to determine if any genes from a 32-gene panel could be used to determine GC prognosis. We carried out a retrospective study on the expression of 32 genes, selected for their proven or potential links to GC, on historic formalin fixed paraffin-embedded (FFPE) GC specimens from our unit. Gene expression was measured using quantitative nuclease protection assays (qNPA) technology. Following statistical analysis of the results, immunohistochemical staining for eight genes, both discriminating and non-discriminating, was conducted in seven age and sex matched non-metastatic: metastatic GC pairings. The stained samples were reviewed by two blinded consultant histopathologists. Multivariate Cox analysis of the gene expression data revealed metastatic status, age, sex and five genes appeared to influence GC survival. Genes negatively influencing survival included and (relative risks 2.20, 3.73 and 7.53 respectively). Genes conveying survival benefit included and (relative risks 0.10 and 0.24 respectively). Immunohistochemical staining of seven age and sex matched non-metastatic: metastatic pairs revealed no association between gene expression and protein expression. Our study found several genes whose expression may affect GC prognosis. However, immunohistochemical analysis revealed no association between gene expression and protein expression. It remains to be determined whether gene expression or protein expression are reliable means of assessing GC prognosis.

A primary aim of detailed analysis of multi-gigapixel histology images is assisting pathologists for better cancer grading and prognostication. Several methods have been proposed for the analysis of histology images in the literature. However, these methods are often limited to the classification of two classes i.e., tumor and stroma. Also, most existing methods are based on fully supervised learning and require a large amount of annotations, which are very difficult to obtain. To alleviate these challenges, we propose a novel community detection algorithm for the classification of tissue in Whole-slide Images (WSIs). The proposed algorithm uses a novel graph-based approach to the problem of detecting prevalent communities in a collection of histology images in an semi-supervised manner resulting the identification of six distinct tissue phenotypes in the multi-gigapixel image data. We formulate the problem of identifying distinct tissue phenotypes as the problem of finding network communities using the geodesic density gradient in the space of potential interaction between different cellular components. We show that prevalent communities found in this way represent distinct and biologically meaningful tissue phenotypes. Experiments on two independent Colorectal Cancer (CRC) datasets demonstrate that the proposed algorithm outperforms current state-of-the-art methods.

Identifying lung adenocarcinoma growth patterns is critical for diagnosis and treatment of lung cancer patients. Growth patterns have variable texture, shape, size and location. They could appear individually or fused together in a way that makes it difficult to avoid inter/intra variability in pathologists reports. Thus, employing a machine learning method to learn these patterns and automatically locate them within the tumour is indeed necessary. This will reduce the effort, assessment variability and provide a second opinion to support pathologies decision. To the best of our knowledge, no work has been done to classify growth patterns in lung adenocarcinoma. In this paper, we propose applying deep learning framework to perform lung adenocarcinoma pattern classification. We investigate what contextual information is adequate for training using patches extracted at several resolutions. We find that both cellular and architectural morphology features are required to achieve the best performance. Therefore, we propose using multi-resolution deep CNN for growth pattern classification in lung adenocarcinoma. Our preliminarily results show an increase in the overall classification accuracy.

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. The inference is made via a Reversible-Jump Markov chain Monte Carlo simulation. To the best of our knowledge, all existing published algorithms for gland segmentation are designed to mainly work on healthy samples, adenomas, and low grade adenocarcinomas. One of them has been demonstrated to work on intermediate grade adenocarcinomas at its best. Our experimental results show that the RPM yields favorable results, both quantitatively and qualitatively, for extraction of glandular structures in histology images of normal human colon tissues as well as benign and cancerous tissues, excluding undifferentiated carcinomas.

In this paper, we present a fast method for registration of multiple large, digitised whole-slide images (WSIs) of serial histology sections. Through cross-slide WSI registration, it becomes possible to select and analyse a common visual field across images of several serial section stained with different protein markers. It is, therefore, a critical first step for any downstream co-localised cross-slide analysis. The proposed registration method uses a two-stage approach, first estimating a fast initial alignment using the tissue sections’ external boundaries, followed by an efficient refinement process guided by key biological structures within the visual field. We show that this method is able to produce a high quality alignment in a variety of circumstances, and demonstrate that the refinement is able to quantitatively improve registration quality. In addition, we provide a case study that demonstrates how the proposed method for cross-slide WSI registration could be used as part of a specific co-expression analysis framework.

Purpose Despite posterior vitreous detachment being a common ocular event affecting most individuals in an aging population, there is little consensus regarding its precise anatomic definition. We investigated the morphologic appearance and molecular composition of the posterior hyaloid membrane to determine whether the structure clinically observed enveloping the posterior vitreous surface after posterior vitreous detachment is a true basement membrane and to postulate its origin. Understanding the relationship between the vitreous (in both its attached and detached state) and the internal limiting membrane of the retina is essential to understanding the cause of rhegmatogenous retinal detachment and vitreoretinal interface disorders, as well as potential future prophylactic and treatment strategies. Design Clinicohistologic correlation study. Participants Thirty-six human donor globes. Methods Vitreous bodies identified to have posterior vitreous detachment were examined with phase-contrast microscopy and confocal microscopy after immunohistochemically staining for collagen IV basement membrane markers, in addition to extracellular proteins that characterize the vitreoretinal junction (fibronectin, laminin) and vitreous gel (opticin) markers. The posterior retina similarly was stained to evaluate the internal limiting membrane. Findings were correlated to the clinical appearance of the posterior hyaloid membrane observed during slit-lamp biomicroscopy after posterior vitreous detachment and compared with previously published studies. Main Outcome Measures Morphologic appearance and molecular composition of the posterior hyaloid membrane. Results Phase-contrast microscopy consistently identified a creased and distinct glassy membranous sheet enveloping the posterior vitreous surface, correlating closely with the posterior hyaloid membrane observed during slit-lamp biomicroscopy in patients with posterior vitreous detachment. Immunofluorescent confocal micrographs demonstrated the enveloping membranous structure identified on phase-contrast microscopy to show positive stain results for type IV collagen. Immunofluorescence of the residual intact internal limiting membrane on the retinal surface also showed positive stain results for type IV collagen. Conclusions The results of this study provide immunohistochemical evidence that the posterior hyaloid membrane is a true basement membrane enveloping the posterior hyaloid surface. Because this membranous structure is observed only after posterior vitreous detachment, the results of this study indicate that it forms part of the internal limiting membrane when the vitreous is in its attached state.

Background Knowledge of the genotype of melanoma is important to guide patient management. Identification of mutations in BRAF and c-KIT lead directly to targeted treatment, but it is also helpful to know if there are driver oncogene mutations in NRAS, GNAQ or GNA11 as these patients may benefit from alternative strategies such as immunotherapy. Methods While polymerase chain reaction (PCR) methods are often used to detect BRAF mutations, next generation sequencing (NGS) is able to determine all of the necessary information on several genes at once, with potential advantages in turnaround time. We describe here an Ampliseq hotspot panel for melanoma for use with the IonTorrent Personal Genome Machine (PGM) which covers the mutations currently of most clinical interest. Results We have validated this in 151 cases of skin and uveal melanoma from our files, and correlated the data with PCR based assessment of BRAF status. There was excellent agreement, with few discrepancies, though NGS does have greater coverage and picks up some mutations that would be missed by PCR. However, these are often rare and of unknown significance for treatment. Conclusions PCR methods are rapid, less time-consuming and less expensive than NGS, and could be used as triage for patients requiring more extensive diagnostic workup. The NGS panel described here is suitable for clinical use with formalin-fixed paraffin-embedded (FFPE) samples. Electronic supplementary material The online version of this article (doi:10.1186/s12885-017-3149-0) contains supplementary material, which is available to authorized users.

Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners.

Background Drug-eluting balloons (DEB) may reduce infrapopliteal restenosis and reintervention rates versus percutaneous transluminal angioplasty (PTA) and improve wound healing/limb preservation. Objectives The goal of this clinical trial was to assess the efficacy and safety of IN.PACT Amphirion drug-eluting balloons (IA-DEB) compared to PTA for infrapopliteal arterial revascularization in patients with critical limb ischemia (CLI). Methods Within a prospective, multicenter, randomized, controlled trial with independent clinical event adjudication and angiographic and wound core laboratories 358 CLI patients were randomized 2:1 to IA-DEB or PTA. The 2 coprimary efficacy endpoints through 12 months were clinically driven target lesion revascularization (CD-TLR) and late lumen loss (LLL). The primary safety endpoint through 6 months was a composite of all-cause mortality, major amputation, and CD-TLR. Results Clinical characteristics were similar between the 2 groups. Significant baseline differences between the IA-DEB and PTA arms included mean lesion length (10.2 cm vs. 12.9 cm; p = 0.002), impaired inflow (40.7% vs. 28.8%; p = 0.035), and previous target limb revascularization (32.2% vs. 21.8%; p = 0.047). Primary efficacy results of IA-DEB versus PTA were CD-TLR of 9.2% versus 13.1% (p = 0.291) and LLL of 0.61 ± 0.78 mm versus 0.62 ± 0.78 mm (p = 0.950). Primary safety endpoints were 17.7% versus 15.8% (p = 0.021) and met the noninferiority hypothesis. A safety signal driven by major amputations through 12 months was observed in the IA-DEB arm versus the PTA arm (8.8% vs. 3.6%; p = 0.080). Conclusions In patients with CLI, IA-DEB had comparable efficacy to PTA. While primary safety was met, there was a trend towards an increased major amputation rate through 12 months compared to PTA. (Study of IN.PACT Amphirion™ Drug Eluting Balloon vs. Standard PTA for the Treatment of Below the Knee Critical Limb Ischemia [INPACT-DEEP]; NCT00941733 )

Introduction (a) Describe the appearance of the lesion shown in Figure 3aed and give your differential diagnosis. Pulmonary spindle cell tumours encompass a range of benign and malignant conditions, including rare entities, which frequently present difficulties in the clinical and pathological diagnosis. Familiaritywith these lesions, and themethods for arriving at the correct diagnosis, are invaluable in ensuring that the patient receives optimum care.

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. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI’2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

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. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the centers of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and potentially lead to a better understanding of cancer. 1982-2012 IEEE. This paper was made possible by NPRP grant number NPRP5-1345-1-228 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. K. Sirinukunwattana acknowledges the partial financial support provided by the Department of Computer Science, University of Warwick, U.K. Scopus

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. Methods A 32 item questionnaire was developed and self-administered to undergraduate medical students. Participants rated the characteristics on a three point scale (0 = not important, 1 = mildly important, 2 = very important). One way ANOVA and student's t-test were used to compare the groups. Results A total of 349 (65.23%) distributed questionnaires were returned. The highest ranked themes were teaching and facilitating learning, patient care and continuing professional development followed by communication and professionalism. Safe environment and guiding personal and professional development was indicated least important. Differences were also observed between scores obtained by males and females. Conclusion Globally there are attributes which are perceived as essential for role models, while others are considered desirable. An understanding of the attributes which are essential and desirable for role models can help medical educators devise strategies which can reinforce those attributes within their institutions.

A 65-year-old lady and a 69-year-old gentleman, both with a background history of adult-onset asthma, presented with clinical features of heart failure (HF). High-sensitivity cardiac troponin T and eosinophils were significantly raised, along with poor left ventricular (LV) systolic function on cardiac imaging. Endocardial and skin biopsy (in cases 1 and 2, respectively) showed eosinophilic infiltration. This in combination with the clinical features confirmed the diagnosis of eosinophilic myocarditis (EM) secondary to eosinophilic granulomatosis with polyangiitis in case 1. Both cases were managed with high-dose intravenous corticosteroids and conventional HF medication. Case 1 successfully responded clinically with improvement in LV systolic function. Case 2 required further immunosuppressive therapy (rituximab) and cardiac resynchronisation therapy, but eventually died of septic shock secondary to immunosuppressives. Our cases highlight the importance of early diagnosis and treatment of EM and ongoing monitoring of patients on immunosuppressive therapy.

Epiretinal membrane (ERM) formation is a common change resulting in disturbance of macular vision and predisposing to rhegmatogenous retinal detachment. Current treatment strategies rely chiefly on surgical removal of the membranes from the surface of the retina, allowing the retina to remodel and reattach. Improved knowledge of the pathological process behind the formation of these membranes, particularly knowledge of the cell types involved in their formation, is likely to increase our understanding of the way this group of diseases behave and to improve treatment. We reviewed the histological findings of 109 surgically removed specimens and correlated these to age-related changes seen in a 32 cadaver eyes studied after corneal harvesting. The samples were studied using light microscopy and immunocytochemistry. In all cases of idiopathic ERMs, including cellophane maculopathy, macular hole, and vitreomacular traction syndrome, laminocytes were the exclusive cell type present. In cases of macular pucker associated with retinal tears, the membranes contain variable cohesive groups of retinal pigment epithelial (RPE) cells in addition to laminocytes. In cases of proliferative diabetic retinopathy, membranes consist almost entirely of capillaries and hyaline stromal tissue, with or without haemosiderin pigment and RPE cells and in which laminocytes and ILM were not identified. In cadaver eyes PVD was seen in 17/32 (53%) of cases, and the vitreous was attached in 14/32 (43.7%) and in one case no vitreous was present. Isolated laminocytes were present on the retinal surface in 12/18 cases with detached vitreous and in 1/14 cases with attached vitreous. In all cases laminocytes were scanty and confined to the optic nerve head, macular or subjacent macular retina. Immunohistochemistry findings indicate that laminocytes are positive for glial fibrillary acidic protein (GFAP), cytokeratin marker AE1/AE3, type II collagen, and type IV collagen. In some cases novel basement membrane formation was seen. There was a tendency for increased positivity of GFAP and AE1/AE3 with increased cellularity, and where novel basement membrane formation was present. Laminocytes are the fundamental cell type in idiopathic ERMs. These cells are frequently found in small and dispersed numbers in eyes containing a PVD. The presence of retinal pigment cells invariable indicates proliferative retinopathy and is only seen in association with a retinal detachment or tear. Diabetic membranes are composed of neovascular stromal tissue, which is most likely to be a response to retinal hypoxia.

NPY is an important central orexigenic hormone, but little is known about its peripheral actions in human adipose tissue (AT) or its potential paracrine effects. Our objective was to examine NPY's role in AT, specifically addressing NPY protein expression, the effect of NPY on adipokine secretion, and the influence of insulin and rosiglitazone (RSG) on adipocyte-derived NPY in vitro. Ex vivo human AT was obtained from women undergoing elective surgery [age: 42.7 ± 1.5 yr (mean ± SE), BMI: 26.2 ± 0.7 kg/m2; n = 38]. Western blot analysis was used to determine NPY protein expression in AT depots. Abdominal subcutaneous (AbSc) adipocytes were isolated and treated with recombinant (rh) NPY, insulin, and RSG. NPY and adipokine levels were measured by ELISA. Our results were that NPY was localized in human AT and adipocytes and confirmed by immunohistochemistry. Depot-specific NPY expression was noted as highest in AbSc AT (1.87 ± 0.23 ODU) compared with omental (Om; 1.03 ± 0.15 ODU, P = 0.029) or thigh AT (Th; 1.0 ± 0.29 ODU, P = 0.035). Insulin increased NPY secretion (control: 0.22 ± 0.024 ng/ml; 1 nM insulin: 0.26 ± 0.05 ng/ml; 100 nM insulin: 0.29 ± 0.04 ng/ml; 1,000 nM insulin: 0.3 ± 0.04 ng/ml; P < 0.05, n = 13), but cotreatment of RSG (10 nM) with insulin (100 nM) had no effect on NPY secretion. Furthermore, adipocyte treatment with rh-NPY downregulated leptin secretion (control: 6.99 ± 0.89 ng/ml; 1 nmol/l rh-NPY: 4.4 ± 0.64 ng/ml; 10 nmol/l rh-NPY: 4.3 ± 0.61 ng/ml, 100 nmol/l rh-NPY: 4.2 ± 0.67 ng/ml; P < 0.05, n = 10) but had no effect on adiponectin or TNF-α secretion. We conclude that NPY is expressed and secreted by human adipocytes. NPY secretion is stimulated by insulin, but this increment was limited by cotreatment with RSG. NPY's antilipolytic action may promote an increase in adipocyte size in hyperinsulinemic conditions. Adipose-derived NPY mediates reduction of leptin secretion and may have implications for central feedback of adiposity signals.

Complete datasets were available in 92.5% of patients treated with SES and 89.1% of patients assigned to BMS. The 1-, 3-, and 5-year rates of survival free from target lesion revascularization (TLR) were, respectively, 99.2%, 93.8%, and 89.7% in the SES group versus 75.9%, 75.0%, and 74.0% in the control group (p 0.001; log-rank). Rates of all MACE at 5 years were 25.8% in patients treated with SES versus 35.2% in patients assigned to BMS (p 0.03; log-rank). Rates of stent thrombosis, per protocol or by the Academic Research Consortium definitions, were similar in both groups. Conclusions The 5-year rate of TLR associated with SES was significantly lower than that with BMS. There was no apparent adverse effect associated with the use of SES, although the trial was not powered to examine uncommon complications. (J Am Coll Cardiol 2007;50:1299‐304) © 2007 by the American College of Cardiology Foundation

Aims:\ud 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. The aim of this study is to provide evidence of non-inferiority.\ud \ud Methods and results:\ud Seventeen pathologists re-reported 3017 cases by DP. Of these, 1009 were re-reported by the same pathologist, and 2008 by a different pathologist. Re-examination of 10 138 scanned slides (2.22 terabytes) produced 72 variances between GS and DP reports, including 21 clinically significant variances. Ground truth lay with GS in 12 cases and with DP in nine cases. These results are within the 95% confidence interval for existing intraobserver and interobserver variability, proving that DP is non-inferior to GS. In three cases, the digital platform was deemed to be responsible for the variance, including a gastric biopsy, where Helicobacter pylori only became visible on slides scanned at the ×60 setting, and a bronchial biopsy and penile biopsy, where dysplasia was reported on DP but was not present on GS.\ud \ud Conclusions:\ud This is one of the largest studies proving that DP is equivalent to GS for the diagnosis of histopathology specimens. Error rates are similar in both platforms, although some problems e.g. detection of bacteria, are predictable.

There have been great advancements in the field of immunofluorescence imaging. The surge in development of analytical methods for such data makes it crucial to develop benchmark synthetic datasets for objectively validating these methods. We propose a model of the healthy colonic crypt microenvironments. Our model can simulate immunofluorescence image data with parameters that allow control over cellularity, cell overlap ratio, image resolution, and objective level. To the best of our knowledge, ours is the first model to simulate immunofluorescence image data at subcellular level for healthy colon tissue, where the cells have several compartments and are organized to mimic the microenvironment of tissue in situ rather than dispersed cells in a cultured environment. Validation of the model has been performed by comparing morphological features of the tissue structure between real and simulated images. In addition, we compare the performance of two cell counting algorithms. The simulated data could also be used to validate techniques such as image restoration, cell segmentation, and crypt segmentation.

In this paper, we present a stochastic model for glandular structures in Hematoxylin and Eosin stained histology images, 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 likelihood about the presence of glandular structure. The inference is made via a Reversible-Jump Markov Chain Monte Carlo simulation. Our experimental results show that the RPM yields favorable results, both quantitatively and qualitatively, for extraction of glandular regions in histology images of human colon tissue.

The first step prior to most analyses on most histopathology images is the detection of area of interest. In this work, we present a superpixel-based approach for glandular structure detection in colon histology images. An image is first segmented into superpixels with the constraint on the presence of glandular boundaries. Texture and color information is then extracted from each superpixel to calculate the probability of that superpixel belonging to glandular regions, resulting in a glandular probability map. In addition, we present a novel texture descriptor derived from a region covariance matrix of scattering coefficients. Our approach shows encouraging results for the detection of glandular structures in colon tissue samples.

Stain separation is the process whereby a full colour histology section image is transformed into a series of single channel images, each corresponding to a given stain's expression. Many algorithms in the field of digital pathology are concerned with the expression of a single stain, thus stain separation is a key preprocessing step in these situations. We present a new versatile method of stain separation. The method uses Independent Component Analysis (ICA) to determine a set of statistically independent vectors, corresponding to the individual stain expressions. In comparison to other popular approaches, such as PCA and NNMF, we found that ICA gives a superior projection of the data with respect to each stain. In addition, we introduce a correction step to improve the initial results provided by the ICA coefficients. Many existing approaches only consider separation of two stains, with primary emphasis on Haematoxylin and Eosin. We show that our method is capable of making a good separation when there are more than two stains present. We also demonstrate our method's ability to achieve good separation on a variety of different stain types.

Detection of epithelial tumor nuclei in standard Hematoxylin & Eosin stained histology images is an essential step for the analysis of tissue architecture. The problem is quite challenging due to the high chromatin texture of the tumor nuclei and their irregular size and shape. In this work, we propose a spatially constrained convolutional neural network (CNN) for the detection of malignant epithelial nuclei in histology images. Given an input patch, the proposed CNN is trained to regress, for every pixel in the patch, the probability of being the center of an epithelial tumor nucleus. The estimated probability values are topologically constrained such that high probability values are concentrated in the vicinity of the center of nuclei. The location of local maxima is then used as a cue for the final detection. Experimental results show that the proposed network outperforms the conventional CNN with center-pixel-only regression for the task of epithelial tumor nuclei detection.

Sirolimus-eluting stents reduce rates of restenosis and reintervention, as compared with uncoated stents. Data are limited regarding the safety and efficacy of such stents in primary percutaneous coronary intervention (PCI) for acute myocardial infarction with ST-segment elevation.We performed a single-blind, multicenter, prospectively randomized trial to compare sirolimus-eluting stents with uncoated stents in primary PCI for acute myocardial infarction with ST-segment elevation. The trial included 712 patients at 48 medical centers. The primary end point was target-vessel failure at 1 year after the procedure, defined as target-vessel-related death, recurrent myocardial infarction, or target-vessel revascularization. A follow-up angiographic substudy was performed at 8 months among 174 patients from selected centers.The rate of the primary end point was significantly lower in the sirolimus-stent group than in the uncoated-stent group (7.3% vs. 14.3%, P=0.004). This reduction was driven by a decrease in the rate of target-vessel revascularization (5.6% and 13.4%, respectively; P0.001). There was no significant difference between the two groups in the rate of death (2.3% and 2.2%, respectively; P=1.00), reinfarction (1.1% and 1.4%, respectively; P=1.00), or stent thrombosis (3.4% and 3.6%, respectively; P=1.00). The degree of neointimal proliferation, as assessed by the mean (+/-SD) in-stent late luminal loss, was significantly lower in the sirolimus-stent group (0.14+/-0.49 mm, vs. 0.83+/-0.52 mm in the uncoated stent group; P0.001).Among selected patients with acute myocardial infarction, the use of sirolimus-eluting stents significantly reduced the rate of target-vessel revascularization at 1 year. (ClinicalTrials.gov number, NCT00232830 [ClinicalTrials.gov].).

Supplemental Digital Content is available in the text., Background— Drug-coated balloons (DCBs) have shown promise in improving the outcomes for patients with peripheral artery disease. We compared a paclitaxel-coated balloon with percutaneous transluminal angioplasty (PTA) for the treatment of symptomatic superficial femoral and popliteal artery disease. Methods and Results— The IN.PACT SFA Trial is a prospective, multicenter, single-blinded, randomized trial in which 331 patients with intermittent claudication or ischemic rest pain attributable to superficial femoral and popliteal peripheral artery disease were randomly assigned in a 2:1 ratio to treatment with DCB or PTA. The primary efficacy end point was primary patency, defined as freedom from restenosis or clinically driven target lesion revascularization at 12 months. Baseline characteristics were similar between the 2 groups. Mean lesion length and the percentage of total occlusions for the DCB and PTA arms were 8.94±4.89 and 8.81±5.12 cm (P=0.82) and 25.8% and 19.5% (P=0.22), respectively. DCB resulted in higher primary patency versus PTA (82.2% versus 52.4%; P

To examine the histological and immunocytochemical characteristics of epiretinal membranes (ERM) associated with stage 4 macular holes (MH) so as to establish a vitreoretinal rationale for surgery in stage 4 MH. Consecutive patients with stage 4 MH undergoing vitrectomy and membrane peeling were recruited. Preoperatively, the eyes were examined for ERM formation over the macula and completeness of posterior hyaloid membrane (PHM) separation from the retina. ERM peel specimens obtained during surgery were sent for histological and immunocytochemical studies and were compared with the PHM specimens taken from a previous post-mortem study of eyes with physiological posterior vitreous detachment but without macular holes. A total of 13 patients with stage 4 MH fulfilled the inclusion criteria and were recruited. Preoperatively, all eyes had an ERM over the macula and incomplete separation of the PHM seen as a defect in the PHM on specular biomicroscopy. Histologically, the ERM specimens had very similar morphological characteristics to PHM, consisting of an eosinophilic membrane of varying thickness with scattered spindle-shaped cells. The membranes stained positively for type IV collagen while the cells were glial fibrillary acidic protein positive. Postoperatively, successful closure of MH was achieved in all cases. Stage 4 MH is characterised by incomplete separation of the PHM from the retina with remnants overlying the macula manifesting as ERM. Removal of the ERM is required during vitrectomy in order to relieve the tangential forces involved in the development of MH.

background Carotid endarterectomy is more effective than medical management in the prevention of stroke in patients with severe symptomatic or asymptomatic atherosclerotic carotidartery stenosis. Stenting with the use of an emboli-protection device is a less invasive revascularization strategy than endarterectomy in carotid-artery disease. methods We conducted a randomized trial comparing carotid-artery stenting with the use of an emboli-protection device to endarterectomy in 334 patients with coexisting conditions that potentially increased the risk posed by endarterectomy and who had either a symptomatic carotid-artery stenosis of at least 50 percent of the luminal diameter or an asymptomatic stenosis of at least 80 percent. The primary end point of the study was the cumulative incidence of a major cardiovascular event at 1 year — a composite of death, stroke, or myocardial infarction within 30 days after the intervention or death or ipsilateral stroke between 31 days and 1 year. The study was designed to test the hypothesis that the less invasive strategy, stenting, was not inferior to endarterectomy. results The primary end point occurred in 20 patients randomly assigned to undergo carotidartery stenting with an emboli-protection device (cumulative incidence, 12.2 percent) and in 32 patients randomly assigned to undergo endarterectomy (cumulative incidence, 20.1 percent; absolute difference, i7.9 percentage points; 95 percent confidence interval, i16.4 to 0.7 percentage points; P=0.004 for noninferiority, and P=0.053 for superiority). At one year, carotid revascularization was repeated in fewer patients who had received stents than in those who had undergone endarterectomy (cumulative incidence, 0.6 percent vs. 4.3 percent; P=0.04). conclusions Among patients with severe carotid-artery stenosis and coexisting conditions, carotid stenting with the use of an emboli-protection device is not inferior to carotid endarterectomy.

PURPOSE The Cordis Randomized Iliac Stent Project–US (CRISP-US) trial evaluated, with an equivalence design, the performance of the shape memory alloy recoverable technology (SMART) nitinol self-expanding stent and the stainless steel Wallstent for treating iliac artery disease after suboptimal percutaneous transluminal angioplasty (PTA). MATERIALS AND METHODS This multicenter, prospective, randomized trial comprised 203 patients with chronic limb ischemia who received either the SMART stent ( n = 102) or the Wallstent ( n = 101) after suboptimal PTA. The primary equivalence end point was a composite of 9-month restenosis, 30-day death, and 9-month target vessel revascularization. Functional, clinical, and hemodynamic assessments were made at hospital discharge and at 1, 6, 9, and 12 months. RESULTS The 9-month composite end point rate was equivalent for the SMART stent and Wallstent (6.9% vs 5.9%), with low rates of restenosis (3.5% vs 2.7%), death (2.0% vs 0.0%), and revascularization (2.0% vs 4.0%) in the two groups. Primary patency at 12 months was 94.7% and 91.1% with the SMART stent and Wallstent, respectively. Functional and hemodynamic improvement was also comparable between the groups. The acute procedural success rate was higher in the SMART stent group (98.2% vs 87.5%; P = .002). The frequency of major adverse events was similar at 1 year (4.9% vs 5.9%). CONCLUSIONS The performance of the SMART stent was equivalent to that of the Wallstent for treating iliac artery stenosis. The design characteristics of the SMART stent may contribute to greater procedural success and more accurate stent deployment.

Evidence is accumulating to suggest that hnRNP B1 expression may be a useful tool in the early diagnosis of lung cancer. This study examined the immunohistochemical expression of hnRNP B1 in archived sections of resected lung cancers and compared the patterns of expression with those seen in similar archived sections of non-neoplastic lung. Particular attention was paid to the expression of hnRNP B1 in the benign bronchial cells in both cases, to establish if overexpression of this protein in respiratory epithelial cells is specific for malignancy. Nineteen cases of different types of non-small cell carcinoma were examined (eight squamous cell, six adenocarcinomas, two carcinosarcomas, two undifferentiated large cell carcinomas, and one mucoepidermoid carcinoma) and compared with sections from 16 open lung biopsies (three cases of cryptogenic fibrosing alveolitis, two cases of sarcoidosis, two cases of organizing pneumonia, and one case each of tuberculosis, extrinsic allergic alveolitis, non-specific interstitial pneumonitis, pneumocystis pneumonia, aspergilloma, respiratory bronchiolitis-interstitial lung disease, mineral dust disease, Sjögren's syndrome and systemic sclerosis vascular variant). All the tumours showed positive staining, with the vast majority, 16/19 (84%), showing strong diffuse nuclear staining. The background cells of these cases showed positive staining in alveolar macrophages, lymph node germinal centres, bronchial mucous glands, and bronchial epithelial cells. No significant difference was seen in the percentage of positive bronchial epithelial cells in bronchi adjacent to the tumour compared with the resection margins. In the benign lung cases, positive bronchial epithelial cells were seen in a small percentage, 3/16 (18%), of cases, but the majority of cases showed no or very focal staining. The levels of expression between benign epithelial cells of malignant cases, compared with benign, showed a significant difference when the staining was assessed in percentage of positive nuclei (p = 0.001, Fisher's exact test). The results confirm that hnRNP B1 is widely expressed in a range of lung carcinomas; that expression is seen in benign bronchial epithelial cells and inflammatory cells; and that expression in background bronchial epithelial cells appears to be higher in malignant than in benign lung disease. It is feasible that this biomarker may be of use in the detection of early lung cancer, provided that levels of expression can be accurately quantified.