Gastric epithelial neoplasm of fundic-gland mucosa lineage: representative of the low atypia differentiated gastric tumor and Ki67 may help in their identification.

Background: Gastric epithelial neoplasm of the fundic-gland mucosa lineages (GEN-FGMLs) are rare forms of gastric tumors that encompass oxyntic gland adenoma (OGA), gastric adenocarcinoma of the fundic-gland type (GA-FG), and gastric adenocarcinoma of the fundic-gland mucosa type (GA-FGM). There is no consensus on the cause, classification, and clinicopathological features of GEN-FGMLs, and misdiagnosis is common because of similarities in symptoms. Methods: 37 cases diagnosed with GEN-FGMLs were included in this study. H&E-stained slides were reviewed and clinicopathological parameters were recorded. Immunohistochemical staining was conducted for MUC2, MUC5AC, MUC6, CD10, CD56, synaptophysin, chromograninA, p53, Ki67, pepsinogen-I, H+/K+-ATPase and Desmin. Results: The patients' ages ranged from 42 to 79 years, with a median age of 60. 17 were male and 20 were female. Morphologically, 19 OGAs, 16 GA-FGs, and two GA-FGMs were identified. Histopathological similarities exist between OGA, GA-FG, and GA-FGM. The tumors demonstrated well-formed glands, expanding with dense growth patterns comprising pale, blue-grey columnar cells with mild nuclear atypia. These cells resembled fundic gland cells. None of the OGA invaded the submucosal layer. The normal gastric pit epithelium covered the entire surface of the OGA and GA-FG, but the dysplasia pit epithelium covered the GA-FGM. Non-atrophic gastritis was observed in more than half of the background mucosa. All cases were diffusely positive for MUC6 and pepsinogen-I on immunohistochemistry. H+/K+-ATPase staining was negative or showed a scattered pattern in most cases. MUC5AC was expressed on the surface of GA-FGMs. p53 was focally expressed and the Ki67 index was low (1%-20%). Compared with OGA, GA-FG and GA-FGM were more prominent in the macroscopic view (p < 0.05) and had larger sizes (p < 0.0001). Additionally, GA-FG and GA-FGM exhibited higher Ki67 indices than OGA (p < 0.0001). Specimens with Ki-67 proliferation indices >2.5% and size >4.5 mm are more likely to be diagnosed with GA-FG and GA-FGM than OGA. Conclusion: GEN-FGMLs are group of well-differentiated gastric tumors with favourable biological behaviours, low cellular atypia, and low proliferation. Immunohistochemistry is critical for confirming diagnosis. Compared with OGA, GA-FG and GA-FGM have larger sizes and higher Ki67 proliferation indices, indicating that they play a critical role in the identification of GEN-FGML. Pathologists and endoscopists should be cautious to prevent misdiagnosis and overtreatment, especially in biopsy specimens.

Multi-attention representation network partial domain adaptation for COVID-19 diagnosis.

The outbreak of COVID-19 threatens the safety of all human beings. Rapid and accurate diagnosis of patients is the effective way to prevent the rapid spread of COVID-19. The current computer-aided diagnosis of COVID-19 requires extensive labeled data for training, and this undoubtedly increases human and material resources costs. Domain adaptation (DA), an existing promising approach, can transfer knowledge from rich labeled pneumonia datasets for COVID-19 diagnosis and classification. However, due to the differences in feature distribution and task semantic between pneumonia and COVID-19, negative transfer may reduce the performance in diagnosis COVID-19 and pneumonia. Furthermore, the training data is usually mixed with many noise samples in practice, and this also poses new challenges for domain adaptation. As a kind of domain adaptation, partial domain adaptation (PDA) can well avoid outlier samples in the source domain and achieve good classification performance in the target domain. However, the existing PDA methods all learn a single feature representation; this can only learn local information about the inputs and ignore other important information in the samples. Therefore multi-attention representation network partial domain adaptation (MARPDA) is proposed in this paper to overcome the above shortcomings of PDA. In MARPDA, we construct the multiple representation networks with attention to acquire the image representation and effectively learn knowledge from different feature spaces. We design the sample-weighted strategy to achieve partial data transfer and address the negative transfer of noise data during training. MARPDA adapts to complex application scenarios and learns fine-grained features of the image from multiple representations. We apply the model to classify pneumonia and COVID-19 respectively, and evaluate it in qualitative and quantitative manners. The experimental results show that our classification accuracy is higher than that of the existing state-of-the-art methods. The stability and reliability of the proposed method are validated by the confusion matrix and the performance curves experiments. In summary, our method has better performance for diagnosis COVID-19 compared to the existing state-of-the-art methods.