AIMC Topic: Stomach Neoplasms

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Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio.

Scientific reports
The tumor-stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients ...

Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in an early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning-based system named ENDOANGEL-LD (lesio...

Robot-assisted Valvuloplastic Esophagogastrostomy by Double-flap Technique Using a Knifeless Linear Stapler After Proximal Gastrectomy.

Surgical laparoscopy, endoscopy & percutaneous techniques
After proximal gastrectomy, valvuloplastic esophagogastrostomy by double-flap technique could be the ideal reconstruction to prevent gastroesophageal reflux. However, it is demanding procedure in laparoscopic surgery. In this video, we demonstrate a ...

Detection of sarcopenic obesity and prediction of long-term survival in patients with gastric cancer using preoperative computed tomography and machine learning.

Journal of surgical oncology
BACKGROUND: Previous studies evaluating the prognostic value of computed tomography (CT)-derived body composition data have included few patients. Thus, we assessed the prevalence and prognostic value of sarcopenic obesity in a large population of ga...

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data.

Computational biology and chemistry
To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of s...

Simultaneous Recognition of Atrophic Gastritis and Intestinal Metaplasia on White Light Endoscopic Images Based on Convolutional Neural Networks: A Multicenter Study.

Clinical and translational gastroenterology
INTRODUCTION: Patients with atrophic gastritis (AG) or gastric intestinal metaplasia (GIM) have elevated risk of gastric adenocarcinoma. Endoscopic screening and surveillance have been implemented in high incidence countries. The study aimed to evalu...

Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-centre, tandem, randomised controlled trial.

The lancet. Gastroenterology & hepatology
BACKGROUND: White light endoscopy is a pivotal first-line tool for the detection of gastric neoplasms. However, gastric neoplasms can be missed during upper gastrointestinal endoscopy due to the subtle nature of these lesions and varying skill among ...

Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: We aimed to develop and validate a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and diffe...