BACKGROUND: A timely diagnosis of early gastric cancer (EGC) can greatly reduce the death rate of patients. However, the manual detection of EGC is a costly and low-accuracy task. The artificial intelligence (AI) method based on deep learning is considered as a potential method to detect EGC. AI methods have outperformed endoscopists in EGC detection, especially with the use of the different region convolutional neural network (RCNN) models recently reported. However, no studies compared the performances of different RCNN series models. OBJECTIVE: This study aimed to compare the performances of different RCNN series models for EGC. METHODS: Three typical RCNN models were used to detect gastric cancer using 3659 gastroscopic images, including 1434 images of EGC: Faster RCNN, Cascade RCNN, and Mask RCNN. RESULTS: The models were evaluated in terms of specificity, accuracy, precision, recall, and AP. Fast RCNN, Cascade RCNN, and Mask RCNN had similar accuracy (0.935, 0.938, and 0.935). The specificity of Cascade RCNN was 0.946, which was slightly higher than 0.908 for Faster RCNN and 0.908 for Mask RCNN. CONCLUSION: Faster RCNN and Mask RCNN place more emphasis on positive detection, and Cascade RCNN places more emphasis on negative detection. These methods based on deep learning were conducive to helping in early cancer diagnosis using endoscopic images.
Background: Hepatocellular carcinoma (HCC) is one of the most malignant tumors and has a poor 5-year survival rate. Family with sequence similarity 83, member D (FAM83D) is characterized as an oncogenic gene related to cell proliferation in many tumors, but the role and underlying mechanism of FAM83D in the development of HCC are still unclear.Methods: FAM83D expression profiles and clinicopathological data were obtained from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC). Additionally, 2 data sets from the Gene Expression Omnibus (GEO) database were used to further validate the FAM83D profile in HCC. We then downregulated the expression of FAM83D in HCC cells transfected with FAM83D small-interfering ribonucleic acid (siRNA) and upregulating its expression by FMA83D-overexpression transfection for further in vitro studies.Results: TCGA and the GEO databases showed that FAM83D was significantly more upregulated in tumor tissues than non-tumor tissues. The high expression of FAM83D in HCC is associated with poor prognostic clinical factors. The knockdown of FAM83D in SNU449 and HUH7 cells in vitro impaired cell proliferation and migration, and promoted apoptosis, while the overexpression of FAM83D in BEL7402 cells had the opposite effect. Further, combined transfection with FBXW7 siRNA or MCL1-overexpression reversed the role of FAM83D knockdown on cell proliferation, migration, and apoptosis in vitro, while FBXW7 expression was negatively correlated with both the FAM83D and MCL1 levels in TCGA-LIHC patients.Conclusions: FAM83D played a significant role in HCC progression by enhancing cell proliferation and migration and inhibiting apoptosis, which may have been caused by the inhibition of the FBXW7/MCL1 signaling pathway. Thus, FAM83D may be a promising therapeutic target for HCC.
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