Risk factors of missed early gastric cancer in endoscopic resected population: a retrospective, case-control study.

BACKGROUND: Missed early gastric cancer (MEGC) is prevalent during esophagogastroduodenoscopy (EGD), which is the first-line recommended strategy for detecting early gastric cancer (EGC). Hence, we explored the risk factors for MEGC and different types of MEGC, based on the endoscopic resected population. METHODS: This retrospective, case-control study was conducted at Nanjing Drum Tower Hospital (NJDTH). We included patients who were diagnosed with EGC during screening EGD, underwent endoscopic resection, and were confirmed by postoperative pathology at the NJDTH from January 2014 to December 2021, and classified them into different types according to the different root causes of misses. Univariable, multivariable, subgroup and propensity score analyses were used to explore the risk factors for MEGC and different types of MEGC. RESULTS: A total of 447 patients, comprising 345 with initially detected early gastric cancer (IDEGC) and 102 with MEGC, were included in this study. Larger size (≥ 1 cm) (OR 0.45, 95% CI 0.27-0.74, P = 0.002) and invasion depth of submucosa (OR 0.26, 95% CI 0.10-0.69, P = 0.007) were negatively associated with MEGC. Use of sedation (OR 0.32, 95% CI 0.20-0.52, P < 0.001) and longer observation time (OR 0.60, 95% CI 0.37-0.96, P = 0.034) exhibited protective effect on MEGC. CONCLUSIONS: Smaller and more superficial EGC lesions are more susceptible to misdiagnosis. The use of sedation and prolonged observation time during EGD could help reduce the occurrence of MEGC.

Feasibility of gallbladder lesion visualization using a novel ultrafine peroral cholangioscopy: A preliminary investigation.

BACKGROUND: Endoscopic visualization of gallbladder lesions by the traditional peroral cholangioscopy (POCS) during the endoscopic retrograde cholangiopancreatography process is challenging. In the present study, we evaluated the feasibility of a newly designed POCS with an ultrafine outer diameter that facilitates gallbladder visualization. METHODS: The novel POCS was designed and manufactured with an outer diameter of 7 French and achieved extremely high performance. The feasibility of this novel POCS for gallbladder observation was assessed in our center between April 2022 and January 2023. The primary outcome was technical success. RESULTS: A total of 16 patients (64.6 ± 18.1 years, 9 males) who underwent novel ultrafine POCS inspection for gallbladder visualization were included. Technical success was achieved in 14 of 16 cases (87.5%); the main reasons for the two unsuccessful inspections were the presence of cystic duct strictures. A total of 1 adverse event occurred, for an overall rate of 6.3%, and there were no serious adverse events during the follow-up. CONCLUSIONS: The results suggest that endoscopic visualization of the gallbladder using the novel ultrafine POCS is feasible. The device is expected to provide a new direction for the management of gallbladder disorders in the future.

The Characteristics and Treatment Outcomes of 71 Duodenal Brunner's Gland Adenomas with Endoscopic Submucosal Dissection.

INTRODUCTION: The aim of this study was to investigate outcomes of patients with duodenal Brunner's gland adenomas (BGAs) that were treated endoscopically. METHODS: We identified 71 consecutive patients treated at our center with endoscopic submucosal dissection (ESD) for their duodenal tumors diagnosed pathologically as BGAs over the period between January 1, 2011 and December 31, 2021. We retrospectively analyzed our experience and short- and long-term outcomes of ESD therapy on patients with BGAs. RESULTS: Among 71 BGA patients with an average age of 57 ± 11.7 years (range: 30-82), 48 (67.6%) were male and 23 (32.4%) were female. The accuracy of preoperative diagnosis with endoscopic ultrasonography was 44.0% (22/50). The H. pylori infection was found in 29 patients (29/71, 40.8%). The median size of BGAs was 1.5 cm (interquartile range [IQR] 0.8-2.7 cm). The most common location was the duodenum bulb (50/71, 64.8%). For the ESD procedure, the median operation time was 15.0 min (IQR 9.5-25.5 min). The en bloc and the complete resection rates were 97.2% and 92.3%, respectively. ESD-related mild acute obstructive pancreatitis was present in 2 patients (2/4, 50%) with BGAs located in the ampulla region. During the follow-up period, 1 patient with a positive peripheral margin experienced tumor recurrence 2 years after the initial ESD. There was no disease-related death for the cohort. CONCLUSION: ESD was an effective and safe therapeutic option for BGA patients with excellent outcomes. Long-term follow-up is needed.

Human-Like Artificial Intelligent System for Predicting Invasion Depth of Esophageal Squamous Cell Carcinoma Using Magnifying Narrow-Band Imaging Endoscopy: A Retrospective Multicenter Study.

INTRODUCTION: Endoscopic evaluation is crucial for predicting the invasion depth of esophagus squamous cell carcinoma (ESCC) and selecting appropriate treatment strategies. Our study aimed to develop and validate an interpretable artificial intelligence-based invasion depth prediction system (AI-IDPS) for ESCC. METHODS: We reviewed the PubMed for eligible studies and collected potential visual feature indices associated with invasion depth. Multicenter data comprising 5,119 narrow-band imaging magnifying endoscopy images from 581 patients with ESCC were collected from 4 hospitals between April 2016 and November 2021. Thirteen models for feature extraction and 1 model for feature fitting were developed for AI-IDPS. The efficiency of AI-IDPS was evaluated on 196 images and 33 consecutively collected videos and compared with a pure deep learning model and performance of endoscopists. A crossover study and a questionnaire survey were conducted to investigate the system's impact on endoscopists' understanding of the AI predictions. RESULTS: AI-IDPS demonstrated the sensitivity, specificity, and accuracy of 85.7%, 86.3%, and 86.2% in image validation and 87.5%, 84%, and 84.9% in consecutively collected videos, respectively, for differentiating SM2-3 lesions. The pure deep learning model showed significantly lower sensitivity, specificity, and accuracy (83.7%, 52.1% and 60.0%, respectively). The endoscopists had significantly improved accuracy (from 79.7% to 84.9% on average, P = 0.03) and comparable sensitivity (from 37.5% to 55.4% on average, P = 0.27) and specificity (from 93.1% to 94.3% on average, P = 0.75) after AI-IDPS assistance. DISCUSSION: Based on domain knowledge, we developed an interpretable system for predicting ESCC invasion depth. The anthropopathic approach demonstrates the potential to outperform deep learning architecture in practice.

Risk factors of perforation in gastric stromal tumors during endoscopic resection: a retrospective case-control study.

BACKGROUND AND AIMS: Perforation is a common complication during endoscopic resection (ER) of gastric gastrointestinal stromal tumors (gGISTs) associated with secondary infections, sepsis, hospitalization time and cost. However, the risk factors of perforation remain controversial. This study aimed to investigate the risk factors for perforation during ER of gGISTs. METHODS: This retrospective case-control study included consecutive patients with gGISTs who underwent ER between June 2009 and November 2021 at the Nanjing Drum Tower Hospital. Univariate and multivariate analyses were performed to investigate the risk factors for perforation. Sensitivity analyses with propensity scoring (PS) were performed to evaluate the stability of the independent effects. RESULTS: In total, 422 patients with gGISTs were included. The following factors were associated with perforation during ER: in the non-intraluminal growth patterns (all confounders adjusted odds ratio [aOR]: 5.39, 95% CI 2.99-9.72, P < 0.001), in the gastric fundus (aOR 2.25, 95% CI 1.40-3.60, P = 0.007), sized ≥ 2 cm (aOR 1.70, 95% CI 1.04-2.77, P = 0.035), in the lesser curvature (aOR 0.12, 95% CI 0.05-0.27, P < 0.001), and in the gastric cardia (aOR 0.13, 95% CI 0.04-0.50, P = 0.003). The PS analysis confirmed the stable independent effects of these identified risk factors. CONCLUSIONS: ERs of gGISTs in non-intraluminal growth patterns, in the gastric fundus, and with larger tumor size were independent risk factors for perforation. While tumors in the lesser curvature or gastric cardia were independent protective factor for perforation.

Sedation Is Associated with Higher Polyp and Adenoma Detection Rates during Colonoscopy: A Retrospective Cohort Study.

Background and Aims: Currently sedation is a common practice in colonoscopy to reduce pain of patients and improve the operator satisfaction, whereas its impact on examination quality, especially adenoma detection rate (ADR) is still controversial. Thus, we aimed to investigate the association of sedation with ADR. Methods: Consecutive patients receiving colonoscopy between January 2017 and January 2020 at the Nanjing Drum Tower Hospital, Nanjing, China, were collected. Univariate and multivariate logistic regression models were performed to investigate the association between sedation and ADR. Subgroup analysis and propensity score matching (PSM) analysis, as sensitivity analysis, were performed to validate the independent effect. Results: The ADR was significantly higher in cases with sedation (ADR: 36.9% vs. 29.1%, odds ratio [OR]: 1.42, 95% confidence interval [CI]: 1.31-1.55, P < 0.001). Multivariate analysis showed that the sedation was an independent factor associated with ADR (OR: 1.49, 95% CI: 1.35-1.65, P < 0.001). The effect was consistent in subgroup analyses (P > 0.05) and PSM analysis (ADR: 37.6% vs. 29.1%, OR: 1.47, 95% CI: 1.33-1.63, P < 0.001). Conclusion: Sedation was associated with a higher polyp and ADR s during colonoscopy, which can promote the quality of colonoscopy.

A real-time interpretable artificial intelligence model for the cholangioscopic diagnosis of malignant biliary stricture (with videos).

BACKGROUND AND AIMS: It is crucial to accurately determine malignant biliary strictures (MBSs) for early curative treatment. This study aimed to develop a real-time interpretable artificial intelligence (AI) system to predict MBSs under digital single-operator cholangioscopy (DSOC). METHODS: A novel interpretable AI system called MBSDeiT was developed consisting of 2 models to identify qualified images and then predict MBSs in real time. The overall efficiency of MBSDeiT was validated at the image level on internal, external, and prospective testing data sets and subgroup analyses, and at the video level on the prospective data sets; these findings were compared with those of the endoscopists. The association between AI predictions and endoscopic features was evaluated to increase the interpretability. RESULTS: MBSDeiT can first automatically select qualified DSOC images with an area under the curve (AUC) of .963 and .968 to .973 on the internal testing data set and the external testing data sets, and then identify MBSs with an AUC of .971 on the internal testing data set, an AUC of .978 to .999 on the external testing data sets, and an AUC of .976 on the prospective testing data set, respectively. MBSDeiT accurately identified 92.3% of MBSs in prospective testing videos. Subgroup analyses confirmed the stability and robustness of MBSDeiT. The AI system achieved superior performance to that of expert and novice endoscopists. The AI predictions were significantly associated with 4 endoscopic features (nodular mass, friability, raised intraductal lesion, and abnormal vessels; P < .05) under DSOC, which is consistent with the endoscopists' predictions. CONCLUSIONS: The study findings suggest that MBSDeiT could be a promising approach for the accurate diagnosis of MBSs under DSOC.

High frequency of colorectal neoplasia in patients with sporadic adenomas or adenocarcinomas of the papilla of Vater: The same adenoma-carcinoma sequence?

BACKGROUND: Data on the frequency of colorectal neoplasia in sporadic ampullary tumors remains scarce. METHODS: We retrospectively reviewed 135 patients undergoing endoscopic ampullectomy from January 2018 to July 2021, and identified 95 patients with sporadic ampullary adenoma who underwent total colonoscopy. Colonoscopy findings were compared with 380 asymptomatic controls using the chi-squared test. Whole-exome sequencing (WES) was performed on one patient with synchronous adenomas of the ampulla of Vater and ascending colon. RESULTS: Colorectal polyps were present in 60% of Cases vs. 34.7% of Controls (P = 0.001), advanced adenoma in 20% vs. 5.5%, and adenocarcinoma in 4.2% vs. 0.8%. Cases tended to have larger polyps than Controls (P<0.001), while there was no difference in polyp location and histology between the two groups. The odds ratio of all the colorectal lesions, advanced colorectal adenoma and adenocarcinoma in Cases was 1.7, 4.2, and 4, respectively. WES in one patient revealed that both of ampullary adenoma and colonic adenoma shared somatic ABCB1 mutation. CONCLUSIONS: The frequency of colorectal polyps or neoplasia was significantly higher in Cases than Controls. We proposed that ampullary neoplasia is analogous to colon lesions and warrants total colonoscopy screening in patients diagnosed with ampullary tumors.

A Retrospective Cohort Study of Factors Influencing Lymph Node Metastasis in Patients With Early Gastric Papillary Adenocarcinoma.

INTRODUCTION: High risk of lymph node metastasis (LNM) in gastric papillary adenocarcinoma causes endoscopists to worry about the suitability of endoscopic resection for early gastric papillary adenocarcinoma (EPAC). We compared risk factors and attempted to establish a scoring system to stratify LNM risk in patients with EPAC. METHODS: A retrospective analysis was performed on 2,513 patients with early gastric carcinoma (EGC) who underwent radical resection in 4 tertiary hospitals in China. Univariate and multivariate analyses were performed to compare the invasiveness in EPAC and other types of EGC and to evaluate potential factors in predicting LNM risk in EPAC groups. RESULTS: Three hundred thirty-five patients with EPAC were enrolled in our study, of which 62 patients were found to have LNM. After comparing clinicopathological characteristics of EPAC with and without LNM, the following factors were included in the risk scoring system: 1 point each for lower stomach location and tumor size >2.0 cm, 3 points for lymphovascular invasion, and 4 points for submucosal invasion; the risk scoring system was validated in a small internal validation set with an area under the curve of 0.844. DISCUSSION: Our results suggested that EPAC was highly invasive compared with other EGCs, especially differentiated EGC types, and need to be treated more rigorously. This proposed risk scoring system could stratify LNM risk in patients with EPAC, and endoscopic resection may only be performed safely on the groups with a low LNM rate.

A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy.

BACKGROUND: Diagnosis of early gastric cancer (EGC) under narrow band imaging endoscopy (NBI) is dependent on expertise and skills. We aimed to elucidate whether artificial intelligence (AI) could diagnose EGC under NBI and evaluate the diagnostic assistance of the AI system. METHODS: In this retrospective diagnostic study, 21,785 NBI images and 20 videos from five centers were divided into a training dataset (13,151 images, 810 patients), an internal validation dataset (7057 images, 283 patients), four external validation datasets (1577 images, 147 patients), and a video validation dataset (20 videos, 20 patients). All the images were labeled manually and used to train an AI system using You look only once v3 (YOLOv3). Next, the diagnostic performance of the AI system and endoscopists were compared and the diagnostic assistance of the AI system was assessed. The accuracy, sensitivity, specificity, and AUC were primary outcomes. RESULTS: The AI system diagnosed EGCs on validation datasets with AUCs of 0.888-0.951 and diagnosed all the EGCs (100.0%) in video dataset. The AI system achieved better diagnostic performance (accuracy, 93.2%, 95% CI, 90.0-94.9%) than senior (85.9%, 95% CI, 84.2-87.4%) and junior (79.5%, 95% CI, 77.8-81.0%) endoscopists. The AI system significantly enhanced the performance of endoscopists in senior (89.4%, 95% CI, 87.9-90.7%) and junior (84.9%, 95% CI, 83.4-86.3%) endoscopists. CONCLUSION: The NBI AI system outperformed the endoscopists and exerted potential assistant impact in EGC identification. Prospective validations are needed to evaluate the clinical reinforce of the system in real clinical practice.

A deep learning-based segmentation system for rapid onsite cytologic pathology evaluation of pancreatic masses: A retrospective, multicenter, diagnostic study.

BACKGROUND: We aimed to develop a deep learning-based segmentation system for rapid on-site cytopathology evaluation (ROSE) to improve the diagnostic efficiency of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) biopsy. METHODS: A retrospective, multicenter, diagnostic study was conducted using 5345 cytopathological slide images from 194 patients who underwent EUS-FNA. These patients were from Nanjing Drum Tower Hospital (109 patients), Wuxi People's Hospital (30 patients), Wuxi Second People's Hospital (25 patients), and The Second Affiliated Hospital of Soochow University (30 patients). A deep convolutional neural network (DCNN) system was developed to segment cell clusters and identify cancer cell clusters with cytopathological slide images. Internal testing, external testing, subgroup analysis, and human-machine competition were used to evaluate the performance of the system. FINDINGS: The DCNN system segmented stained cells from the background in cytopathological slides with an F1-score of 0·929 and 0·899-0·938 in internal and external testing, respectively. For cancer identification, the DCNN system identified images containing cancer clusters with AUCs of 0·958 and 0·948-0·976 in internal and external testing, respectively. The generalizable and robust performance of the DCNN system was validated in sensitivity analysis (AUC > 0·900) and was superior to that of trained endoscopists and comparable to cytopathologists on our testing datasets. INTERPRETATION: The DCNN system is feasible and robust for identifying sample adequacy and pancreatic cancer cell clusters. Prospective studies are warranted to evaluate the clinical significance of the system. FUNDING: Jiangsu Natural Science Foundation; Nanjing Medical Science and Technology Development Funding; National Natural Science Foundation of China.

Engineering hydrogels with homogeneous mechanical properties for controlling stem cell lineage specification.

The extracellular matrix (ECM) is mechanically inhomogeneous due to the presence of a wide spectrum of biomacromolecules and hierarchically assembled structures at the nanoscale. Mechanical inhomogeneity can be even more pronounced under pathological conditions due to injury, fibrogenesis, or tumorigenesis. Although considerable progress has been devoted to engineering synthetic hydrogels to mimic the ECM, the effect of the mechanical inhomogeneity of hydrogels has been widely overlooked. Here, we develop a method based on host-guest chemistry to control the homogeneity of maleimide-thiol cross-linked poly(ethylene glycol) hydrogels. We show that mechanical homogeneity plays an important role in controlling the differentiation or stemness maintenance of human embryonic stem cells. Inhomogeneous hydrogels disrupt actin assembly and lead to reduced YAP activation levels, while homogeneous hydrogels promote mechanotransduction. Thus, the method we developed to minimize the mechanical inhomogeneity of hydrogels may have broad applications in cell culture and tissue engineering.

A Novel Deep Learning System for Diagnosing Early Esophageal Squamous Cell Carcinoma: A Multicenter Diagnostic Study.

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.

A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video).

BACKGROUND AND AIMS: Prediction of intramucosal gastric cancer (GC) is a big challenge. It is not clear whether artificial intelligence could assist endoscopists in the diagnosis. METHODS: A deep convolutional neural networks (DCNN) model was developed via retrospectively collected 3407 endoscopic images from 666 gastric cancer patients from two Endoscopy Centers (training dataset). The DCNN model's performance was tested with 228 images from 62 independent patients (testing dataset). The endoscopists evaluated the image and video testing dataset with or without the DCNN model's assistance, respectively. Endoscopists' diagnostic performance was compared with or without the DCNN model's assistance and investigated the effects of assistance using correlations and linear regression analyses. RESULTS: The DCNN model discriminated intramucosal GC from advanced GC with an AUC of 0.942 (95% CI, 0.915-0.970), a sensitivity of 90.5% (95% CI, 84.1%-95.4%), and a specificity of 85.3% (95% CI, 77.1%-90.9%) in the testing dataset. The diagnostic performance of novice endoscopists was comparable to those of expert endoscopists with the DCNN model's assistance (accuracy: 84.6% vs. 85.5%, sensitivity: 85.7% vs. 87.4%, specificity: 83.3% vs. 83.0%). The mean pairwise kappa value of endoscopists was increased significantly with the DCNN model's assistance (0.430-0.629 vs. 0.660-0.861). The diagnostic duration reduced considerably with the assistance of the DCNN model from 4.35s to 3.01s. The correlation between the perseverance of effort and diagnostic accuracy of endoscopists was diminished using the DCNN model (r: 0.470 vs. 0.076). CONCLUSIONS: An AI-assisted system was established and found useful for novice endoscopists to achieve comparable diagnostic performance with experts.

Differential prognostic implications of gastric adenocarcinoma based on Lauren's classification: a Surveillance, Epidemiology, and End Results (SEER)-based cohort study.

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, 1.15-1.20). 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.

Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study.

BACKGROUND: The study aimed to construct an intelligent difficulty scoring and assistance system (DSAS) for endoscopic retrograde cholangiopancreatography (ERCP) treatment of common bile duct (CBD) stones. METHODS: 1954 cholangiograms were collected from three hospitals for training and testing the DSAS. The D-LinkNet34 and U-Net were adopted to segment the CBD, stones, and duodenoscope. Based on the segmentation results, the stone size, distal CBD diameter, distal CBD arm, and distal CBD angulation were estimated. The performance of segmentation and estimation was assessed by mean intersection over union (mIoU) and average relative error. A technical difficulty scoring scale, which was used for assessing the technical difficulty of CBD stone removal, was developed and validated. We also analyzed the relationship between scores evaluated by the DSAS and clinical indicators including stone clearance rate and need for endoscopic papillary large-balloon dilation (EPLBD) and lithotripsy. RESULTS: The mIoU values of the stone, CBD, and duodenoscope segmentation were 68.35 %, 86.42 %, and 95.85 %, respectively. The estimation performance of the DSAS was superior to nonexpert endoscopists. In addition, the technical difficulty scoring performance of the DSAS was more consistent with expert endoscopists than two nonexpert endoscopists. A DSAS assessment score ≥ 2 was correlated with lower stone clearance rates and more frequent EPLBD. CONCLUSIONS: An intelligent DSAS based on deep learning was developed. The DSAS could assist endoscopists by automatically scoring the technical difficulty of CBD stone extraction, and guiding the choice of therapeutic approach and appropriate accessories during ERCP.

Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study.

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).

Correction to: Low levels of pyruvate induced by a positive feedback loop protects cholangiocarcinoma cells from apoptosis.

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The SIRT2/cMYC Pathway Inhibits Peroxidation-Related Apoptosis In Cholangiocarcinoma Through Metabolic Reprogramming.

Cholangiocarcinoma (CCA) is a malignant cancer with an unknown etiology and an unfavorable prognosis. Most patients are diagnosed at an advanced stage, thus making it essential to find novel curative targets for CCA. Metabolic reprogramming of the tumor cells includes metabolic abnormalities in glucose (known as the Warburg effect) and other substances such as amino acids and fats. Metabolic reprogramming produces anti-oxidant substances, reduces tumor oxidative stress, and finally promotes the proliferation of tumors. There is increasing evidence to imply that SIRT2, a histone deacetylase, and its downstream target cMYC, play metabolic regulatory roles in tumor cells. However, the role of the SIRT2/cMYC pathway in CCA is unclear. To assess the metabolic reprogramming function of the SIRT2/cMYC pathway in CCA and to determine the downstream targets as well as evaluate the therapeutic effect, the CCA RNA-Seq data were downloaded from the TCGA database. Differentially expressed genes were confirmed and KEGG pathway enrichment analysis was performed. Overall, 48 paired CCA samples were collected and subjected to immunohistochemical detection, and the clinical characteristics of participants were summarized. The CCA cells were suppressed or overexpressed with different downstream targets of SIRT2 and then subjected to apoptosis, immunoblotting, seahorse, and metabolites tracing analysis. In vivo experiments were also performed. We found that the SIRT2/cMYC pathway contributed to the proliferation of CCA cells and confirmed that the downstream target is PHDA1 and the serine synthesis pathway. The up-regulated SIRT2 and cMYC levels resulted in low levels of mitochondrial oxidative phosphorylation and increased conversion of glucose to serine and led to poor patient survival. The highly active SIRT2/cMYC pathway up-regulated the serine synthesis pathway pyruvate and increased antioxidant production, thus consequently protecting the CCA cells from oxidative stress-induced apoptosis. Our data revealed that the SIRT2/cMYC pathway plays a critical role in transforming glucose oxidative metabolism to serine anabolic metabolism, thus providing antioxidants for stress resistance. SIRT2/cMYC-induced metabolic reprogramming may represent a new therapeutic target for treating CCA.