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).

DDX3X interacts with SIRT7 to promote PD-L1 expression to facilitate PDAC progression.

Pancreatic ductal adenocarcinoma (PDAC) is recognized as the most aggressive and fatal malignancy. A previous study reported that PDAC patients who exhibit elevated levels of DDX3X have a poor prognosis and low overall survival rate. However, the underlying molecular mechanism remains unclear.

Monoallelic deleterious MUTYH mutations generate colorectal cancer: A case report.

Here we reported a particular case of MUTYH-associated polyposis (MAP) that had only one rare heterozygous variant, but some particular clinical manifestations contributed to occur in this male patient by only one defective MUTYH allele were worth of further investigation. We reported a case of MAP. It is about a 33-year-old man with chief complaints of hematochezia who had multiple polyps that were found in his colon via colonoscopy.

Lactulose vs Polyethylene Glycol for Bowel Preparation: A Single-Center, Prospective, Randomized Controlled Study Based on BMI.

Introduction: Colonoscopy is currently considered as one of the principal techniques to diagnose the colorectal diseases. Admittedly, qualified bowel preparation before colonoscopy is a premise for high-quality examination. Lower quality bowel preparation might seriously impede visualization of the intestinal mucosa, resulting in missed and misdiagnosed intestinal lesions.

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.

Comparison of Modified Cap-Assisted Endoscopic Mucosal Resection and Endoscopic Submucosal Dissection in Treating Intraluminal Gastric Gastrointestinal Stromal Tumor (≤20 mm).

Introduction: A modified cap-assisted endoscopic mucosal resection (mEMR-C), introduced in this study, was a novel variation of the standard EMR. We aimed to compare the outcomes of mEMR-C and endoscopic submucosal dissection (ESD) for the treatment of small (≤20 mm) intraluminal gastric gastrointestinal stromal tumors (gGISTs).

Methods: This retrospective study included 43 patients who underwent mEMR-C and 156 patients who received ESD at Nanjing Drum Tower Hospital.

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.

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.

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.

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).

Diagnostic value of endoscopic ultrasound-guided fine needle aspiration with rapid on-site evaluation performed by endoscopists in solid pancreatic lesions: A prospective, randomized controlled trial.

Background And Aim: Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is the most established diagnostic method for pancreatic tissue. Rapid on-site evaluation by a trained endoscopist (self-ROSE) can improve the diagnostic accuracy. This research is aimed to analyze the application value of self-ROSE for EUS-FNA in solid pancreatic lesions.

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).

Pancreatic and gut hormone responses to mixed meal test in post-chronic pancreatitis diabetes mellitus.

Objective: More than one-third of chronic pancreatitis patients will eventually develop diabetes, recently classified as post-chronic pancreatitis diabetes mellitus (PPDM-C). This study was aimed to investigate the pancreatic and gut hormone responses to a mixed meal test in PPDM-C patients, compared with non-diabetic chronic pancreatitis (CP), and type 2 diabetes patients or healthy controls.

Design And Methods: Sixteen patients with PPDM-C, 12 with non-diabetic CP as well as 10 with type 2 diabetes and healthy controls were recruited.

Comparison of endoscopic full-thickness resection and cap-assisted endoscopic full-thickness resection in the treatment of small (≤1.5 cm) gastric GI stromal tumors.

Background And Aims: With the increasing incidence of small GI stromal tumors (GISTs), endoscopic full-thickness resection (EFTR) and cap-assisted EFTR (EFTR-C) have been suggested as 2 effective resection methods. We aimed to compare the outcomes of EFTR and EFTR-C for the treatment of small (≤1.5 cm) gastric GISTs.

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.

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.

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 retrospectively collected 3407 endoscopic images from 666 gastric cancer patients from two Endoscopy Centers (training dataset).

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.