The separation of benign from malignant mesothelial proliferations is an important clinical but often a difficult morphologic problem. Over the last roughly 10 years a variety of new markers that aid in this separation have been published and some older recommended markers reconsidered. Unlike previous, and largely unusable, empiric immunohistochemical (IHC) stains, these new markers, some using IHC and some using fluourescent in situ hybridization (FISH), are largely based on documented genomic abnormalities in malignant mesotheliomas. However, no marker works in all situations; rather, markers need to be chosen by the morphology of the process in question (epithelial vs. spindled) and the body cavity of interest (pleural vs. peritoneal). It is also important to be familiar with the exact pattern, for example nuclear versus cytoplasmic loss, that indicates a positive test. Furthermore, no single marker is 100% sensitive even with the optimal morphology/location, so that combinations of markers are essential. This review covers the various new markers in the literature, highlights their advantages and limitations, and suggests morphology/site specific combinations that can produce sensitivities in the 80% to 90% (and perhaps higher) range. At present only BRCA-1 related protein-1 and methylthioadenosine phosphorylase IHC, and cyclin-dependent kinase inhibitor 2A (p16) FISH have sufficient publications and reproducibility of results to be considered as established markers. 5-Hydroxymethyl cytosine, enhancer of zeste homolog 2, cyclin D1, and programmed death-ligand 1 IHC, and NF2 FISH are all potentially useful but need further study. The newly described entity of malignant mesothelioma in situ sits at the interface of benign and malignant mesothelial process; criteria for this diagnosis are reviewed.
Deep learning‐based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks to synthesize high‐resolution pathology images of 10 histological types of cancer, including five cancer types from The Cancer Genome Atlas and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board‐certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer‐aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g. rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey (http://gan.aimlab.ca/). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Malignant mesothelioma can be difficult to distinguish from other malignancies, particularly non–small cell lung carcinomas (NSCLCs), without immunohistochemistry. However, conventional markers of mesothelial lineage all have variable degrees of cross-reactivity with other neoplasms, including NSCLCs, necessitating the use of multiple mesothelioma and carcinoma markers in every case for accurate diagnosis. A recently described monoclonal HEG homolog 1 (HEG1) antibody was proposed to be a specific marker for mesothelioma. Here we performed a large scale assessment of the SKM9-2 HEG1 antibody using tissue microarrays containing 69 epithelioid mesotheliomas, 32 sarcomatoid mesotheliomas, 167 NSCLCs, and 17 ovarian high-grade serous carcinomas. Strong membrane staining, usually diffuse, for HEG1 was seen in 65/69 (94%) epithelioid mesotheliomas, 0/60 pulmonary squamous cell carcinomas, 0/73 pulmonary adenocarcinomas, and 0/13 pulmonary large cell carcinomas. HEG1 showed staining in 14/32 (44%) sarcomatoid mesotheliomas compared with 0/21 sarcomatoid pulmonary carcinomas. Three of 17 (18%) high-grade serous carcinomas demonstrated membrane staining. Ten B3 thymoma whole sections were negative. On the microarrays, the conventional mesothelial markers calretinin, WT1, D2-40, and CK5/6 had sensitivities for epithelioid mesothelioma of 94%, 90%, 96%, and 91%, respectively. We conclude that HEG1 SKM9-2 antibody offers sensitivity comparable to conventional markers for epithelioid mesotheliomas, but provides considerably better specificity, such that the diagnosis of epithelioid mesothelioma versus NSCLC potentially could be confirmed with a combination of HEG1 and a suitable broad spectrum carcinoma marker such as claudin-4. HEG1 is specific but insensitive for separating sarcomatoid mesotheliomas from sarcomatoid lung carcinomas.
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