Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network.

BACKGROUND: A convolutional neural network (CNN) is a deep learning algorithm based on the principle of human brain visual cortex processing and image recognition. AIM: To automatically identify the invasion depth and origin of esophageal lesions based on a CNN. METHODS: A total of 1670 white-light images were used to train and validate the CNN system. The method proposed in this paper included the following two parts: (1) Location module, an object detection network, locating the classified main image feature regions of the image for subsequent classification tasks; and (2) Classification module, a traditional classification CNN, classifying the images cut out by the object detection network. RESULTS: The CNN system proposed in this study achieved an overall accuracy of 82.49%, sensitivity of 80.23%, and specificity of 90.56%. In this study, after follow-up pathology, 726 patients were compared for endoscopic pathology. The misdiagnosis rate of endoscopic diagnosis in the lesion invasion range was approximately 9.5%; 41 patients showed no lesion invasion to the muscularis propria, but 36 of them pathologically showed invasion to the superficial muscularis propria. The patients with invasion of the tunica adventitia were all treated by surgery with an accuracy rate of 100%. For the examination of submucosal lesions, the accuracy of endoscopic ultrasonography (EUS) was approximately 99.3%. Results of this study showed that EUS had a high accuracy rate for the origin of submucosal lesions, whereas the misdiagnosis rate was slightly high in the evaluation of the invasion scope of lesions. Misdiagnosis could be due to different operating and diagnostic levels of endoscopists, unclear ultrasound probes, and unclear lesions. CONCLUSION: This study is the first to recognize esophageal EUS images through deep learning, which can automatically identify the invasion depth and lesion origin of submucosal tumors and classify such tumors, thereby achieving good accuracy. In future studies, this method can provide guidance and help to clinical endoscopists.

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network.

Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion statuses of the esophageal diseases and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors, and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification and lesion-specific segmentation network is proposed for automatic esophageal lesion annotation at pixel level. For the established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.9034, specificity of 0.9718, and accuracy of 0.9628, and the segmentation with sensitivity of 0.8018, specificity of 0.9655, and accuracy of 0.9462. All of these indicate that our method enables an efficient, accurate, and reliable esophageal lesion diagnosis in clinics.

Acetyl-11-keto-ß-boswellic acid inhibits proliferation and induces apoptosis of gastric cancer cells through the phosphatase and tensin homolog /Akt/ cyclooxygenase-2 signaling pathway.

BACKGROUND: Gastric cancer is one of the most common malignant tumors of the digestive system worldwide, posing a serious danger to human health. Cyclooxygenase (COX)-2 plays an important role in the carcinogenesis and progression of gastric cancer. Acetyl-11-keto-ß-boswellic acid (AKBA) is a promising drug for cancer therapy, but its effects and mechanism of action on human gastric cancer remain unclear. AIM: To evaluate whether the phosphatase and tensin homolog (PTEN)/Akt/COX-2 signaling pathway is involved in the anti-tumor effect of AKBA in gastric cancer. METHODS: Human poorly differentiated BGC823 and moderately differentiated SGC7901 gastric cancer cells were routinely cultured in Roswell Park Memorial Institute 1640 medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. Gastric cancer cell proliferation was determined by methyl thiazolyl tetrazolium colorimetric assay. Apoptosis was measured by flow cytometry. Cell migration was assessed using the wound-healing assay. Expression of Bcl-2, Bax, proliferating cell nuclear antigen, PTEN, p-Akt, and COX-2 were detected by Western blot analysis. A xenograft nude mouse model of human gastric cancer was established to evaluate the anti-cancer effect of AKBA in vivo. RESULTS: AKBA significantly inhibited the proliferation of gastric cancer cells in a dose- and time-dependent manner, inhibited migration in a time-dependent manner, and induced apoptosis in a dose-dependent manner in vitro; it also inhibited tumor growth in vivo. AKBA up-regulated the expression of PTEN and Bax, and down-regulated the expression of proliferating cell nuclear antigen, Bcl-2, p-Akt, and COX-2 in a dose-dependent manner. The PTEN inhibitor bpv (Hopic) reversed the high expression of PTEN and low expression of p-Akt and COX-2 that were induced by AKBA. The Akt inhibitor MK2206 combined with AKBA down- regulated the expression of p-Akt and COX-2, and the combined effect was better than that of AKBA alone. CONCLUSION: AKBA inhibits the proliferation and migration and promotes the apoptosis of gastric cancer cells through the PTEN/Akt/COX-2 signaling pathway.

Myrrh induces the apoptosis and inhibits the proliferation and migration of gastric cancer cells through down-regulating cyclooxygenase-2 expression.

OBJECTIVE: The present study is designed to evaluate the anti-tumor effects of myrrh on human gastric cancer both in vitro and in vivo. METHODS: The gastric cancer cell proliferation was determined by MTT assay. Apoptosis was measured by flow cytometry and Hoechst 33342 staining. Wound healing was performed to evaluate the effects of myrrh on the migration. COX-2, PCNA, Bcl-2, and Bax expressions were detected by Western blot analysis. A xenograft nude mice model of human gastric cancer was established to evaluate the anti-cancer effect of myrrh in vivo. RESULTS: Myrrh significantly inhibited cellular proliferation, migration, and induced apoptosis in vitro as well as inhibited tumor growth in vivo. In addition, myrrh inhibited the expression of PCNA, COX-2, and Bcl-2 as well as increased Bax expression in gastric cancer cells. CONCLUSION: Myrrh may inhibit the proliferation and migration of gastric cancer cells, as well as induced their apoptosis by down-regulating the expression of COX-2.

Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network.

BACKGROUND: Using deep learning techniques in image analysis is a dynamically emerging field. This study aims to use a convolutional neural network (CNN), a deep learning approach, to automatically classify esophageal cancer (EC) and distinguish it from premalignant lesions. METHODS: A total of 1,272 white-light images were adopted from 748 subjects, including normal cases, premalignant lesions, and cancerous lesions; 1,017 images were used to train the CNN, and another 255 images were examined to evaluate the CNN architecture. Our proposed CNN structure consists of two subnetworks (O-stream and P-stream). The original images were used as the inputs of the O-stream to extract the color and global features, and the pre-processed esophageal images were used as the inputs of the P-stream to extract the texture and detail features. RESULTS: The CNN system we developed achieved an accuracy of 85.83%, a sensitivity of 94.23%, and a specificity of 94.67% after the fusion of the 2 streams was accomplished. The classification accuracy of normal esophagus, premalignant lesion, and EC were 94.23%, 82.5%, and 77.14%, respectively, which shows a better performance than the Local Binary Patterns (LBP) + Support Vector Machine (SVM) and Histogram of Gradient (HOG) + SVM methods. A total of 8 of the 35 (22.85%) EC lesions were categorized as premalignant lesions because of their slightly reddish and flat lesions. CONCLUSIONS: The CNN system, with 2 streams, demonstrated high sensitivity and specificity with the endoscopic images. It obtained better detection performance than the currently used methods based on the same datasets and has great application prospects in assisting endoscopists to distinguish esophageal lesion subclasses.

Metabolic Perturbation and Potential Markers in Patients with Esophageal Cancer.

Clinical diagnosis of esophageal cancer (EC) at early stage is rather difficult. This study aimed to profile the molecules in serum and tissue and identify potential biomarkers in patients with EC. A total of 64 volunteers were recruited, and 83 samples (24 EC serum samples, 21 serum controls, 19 paired EC tissues, and corresponding tumor-adjacent tissues) were analyzed. The gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) was employed, and principal component analysis was used to reveal the discriminatory metabolites and identify the candidate markers of EC. A total of 41 in serum and 36 identified compounds in tissues were relevant to the malignant prognosis. A marked metabolic reprogramming of EC was observed, including enhanced anaerobic glycolysis and glutaminolysis, inhibited tricarboxylic acid (TCA) cycle, and altered lipid metabolism and amino acid turnover. Based on the potential markers of glucose, glutamic acid, lactic acid, and cholesterol, the receiver operating characteristic (ROC) curves indicated good diagnosis and prognosis of EC. EC patients showed distinct reprogrammed metabolism involved in glycolysis, TCA cycle, glutaminolysis, and fatty acid metabolism. The pivotal molecules in the metabolic pathways were suggested as the potential markers to facilitate the early diagnosis of human EC.