Background We aimed to develop and validate a real-time deep convolutional neural networks (DCNNs) system for detecting early gastric cancer (EGC). Methods All 45,240 endoscopic images from 1364 patients were divided into a training dataset (35823 images from 1085 patients) and a validation dataset (9417 images from 279 patients). Another 1514 images from three other hospitals were used as external validation. We compared the diagnostic performance of the DCNN system with endoscopists, and then evaluated the performance of endoscopists with or without referring to the system. Thereafter, we evaluated the diagnostic ability of the DCNN system in video streams. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value and Cohen's kappa coefficient were measured to assess the detection performance. Finding The DCNN system showed good performance in EGC detection in validation datasets, with accuracy (85.1%–91.2%), sensitivity (85.9%–95.5%), specificity (81.7%–90.3%), and AUC (0.887–0.940). The DCNN system showed better diagnostic performance than endoscopists and improved the performance of endoscopists. The DCNN system was able to process oesophagogastroduodenoscopy (OGD) video streams to detect EGC lesions in real time. Interpretation We developed a real-time DCNN system for EGC detection with high accuracy and stability. Multicentre prospective validation is needed to acquire high-level evidence for its clinical application. Funding This work was supported by the National Natural Science Foundation of China (grant nos. 81672935 and 81871947), Jiangsu Clinical Medical Center of Digestive System Diseases and Gastrointestinal Cancer (grant no. YXZXB2016002), and Nanjing Science and Technology Development Foundation (grant no. 2017sb332019).

INTRODUCTION: This study aims to construct a real-time deep convolutional neural networks (DCNNs) system to diagnose early esophageal squamous cell carcinoma (ESCC) with white light imaging endoscopy. METHODS: A total of 4,002 images from 1,078 patients were used to train and cross-validate the DCNN model for diagnosing early ESCC. The performance of the model was further tested with independent internal and external validation data sets containing 1,033 images from 243 patients. The performance of the model was then compared with endoscopists. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and Cohen kappa coefficient were measured to assess performance. RESULTS: The DCNN model had excellent performance in diagnosing early ESCC with a sensitivity of 0.979, a specificity of 0.886, a positive predictive value of 0.777, a negative predictive value of 0.991, and an area under curve of 0.954 in the internal validation data set. The model also depicted a tremendously generalized performance in 2 external data sets and exhibited superior performance compared with endoscopists. The performance of the endoscopists was markedly elevated after referring to the predictions of the DCNN model. An open-accessed website of the DCNN system was established to facilitate associated research. DISCUSSION: A real-time DCNN system, which was constructed to diagnose early ESCC, showed good performance in validation data sets. However, more prospective validation is needed to understand its true clinical significance in the real world.

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Background: Our study aims to analyze the association between Lauren's classification and gastric adenocarcinoma prognosis using comprehensive statistical analyses.Methods: According to the selection criteria, patients were included from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression, propensity score matching, and a multivariate competing risk model were used to investigate the association between Lauren's classification and prognosis. Subgroup analysis was used to investigate the role of confounding factors on the association between Lauren types and prognosis.Results: After exclusion, a total of 20,218 patients from the SEER database were included, with 14,374 intestinal types and 5,844 diffuse types. The univariate Cox regression analysis revealed that the diffuse type had a poorer cancer-specific survival (CSS) rate [hazard ratio (HR), 1.44; 95% confidence interval (CI), 1.38-1.50]. After adjusting for confounding variables, the diffuse type also showed a higher risk of cancer-specific death (HR, 1.20; 95% CI,. Sensitivity analysis showed that after propensity score matching, the diffuse type had a poorer CSS rate (HR, 1.23; 95% CI, 1.10-1.36), and the competing risk model further validated these results [subdistribution HR (SHR), 1.32; 95% CI, 1.23-1.41]. Moreover, subgroup analysis demonstrated stable results in the subgroups, except for patients with T1 stage (HR, 1.06; 95% CI, 0.87-1.28) and a tumor size <2 cm (HR, 1.00; 95% CI, 0.83-1.21).Conclusions: Diffuse-type gastric adenocarcinoma had an overall poorer prognosis compared to the intestinal type. However, in patients with T1 stage and tumor size <2 cm, the diffuse type had a comparable survival rate with the intestinal type.

Objectives: Endoscopic ultrasound-guided gastroenterostomy (EUS-GE) has recently been employed as a novel treatment for gastric outlet obstruction (GOO). The aim of this study was to evaluate the safety and efficacy of EUS-GE for GOO at different sites.