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Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric atrophy (GA) and gastric intestinal metaplasia (IM) of the mucosa of the stomach have been found to increase the risk of gastric cancer and are considered precancerous lesions. Therefore, the early detection of GA and IM may have a valuable role in histopathological risk assessment. However, GA and IM are difficult to confirm endoscopically and, following the Sydney protocol, their diagnosis depends on the analysis of glandular morphology and on the identification of at least one well-defined goblet cell in a set of hematoxylin and eosin (H&E) -stained biopsy samples. To this end, the precise segmentation and classification of glands from the histological images plays an important role in the diagnostic confirmation of GA and IM. In this paper, we propose a digital pathology end-to-end workflow for gastric gland segmentation and classification for the analysis of gastric tissues. The proposed GAGL-VTNet, initially, extracts both global and local features combining multi-scale feature maps for the segmentation of glands and, subsequently, it adopts a vision transformer that exploits the visual dependences of the segmented glands towards their classification. For the analysis of gastric tissues, segmentation of mucosa is performed through an unsupervised model combining energy minimization and a U-Net model. Then, features of the segmented glands and mucosa are extracted and analyzed. To evaluate the efficiency of the proposed methodology we created the GAGL dataset consisting of 85 WSI, collected from 20 patients. The results demonstrate the existence of significant differences of the extracted features between normal, GA and IM cases. The proposed approach for gland and mucosa segmentation achieves an object dice score equal to 0.908 and 0.967 respectively, while for the classification of glands it achieves an F1 score equal to 0.94 showing great potential for the automated quantification and analysis of gastric biopsies. [ABSTRACT FROM AUTHOR]

Although researchers have identified genetic alterations that contribute to development of esophageal adenocarcinoma, we know little about features of patients or environmental factors that mediate progression of chronic acid biliary reflux to Barrett’s esophagus and cancer. Increasing our understanding of the mechanisms by which normal squamous epithelium progresses to early-stage invasive cancer will help formulate rational surveillance guidelines and allow us to divest resources away from patients at low risk of malignancy. We review the cellular and genetic alterations that occur during progression of Barrett’s esophagus, based on findings from clinical studies and mouse models of disease. We review the features of the luminal and mucosal microenvironment of Barrett’s esophagus that promote, in a small proportion of patients, development of esophageal adenocarcinoma. Markers of clonal evolution can be used to determine patient risk for cancer and set surveillance intervals. [ABSTRACT FROM AUTHOR]