AIMC Topic: Stomach Neoplasms
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Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma.
Journal of gastroenterology and hepatology
Jun 27, 2020
BACKGROUND AND AIM: Conventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited because early lesions are asymptomatic, and the associated changes in the mucosa are subtle. There ...
Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.
The journal of pathology. Clinical research
Jun 27, 2020
The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we p...
Tissue Array Analysis
Antigens, Differentiation, Myelomonocytic
CD8 Antigens
Image Interpretation, Computer-Assisted
Immunohistochemistry
Antigens, CD20
Ki-67 Antigen
Predictive Value of Tests
Antigens, CD
Neoplasm Staging
Risk Factors
Stomach Neoplasms
Tumor Microenvironment
Biomarkers, Tumor
Risk Assessment
Deep Learning
Time Factors
Cell Proliferation
Humans
Utilizing artificial intelligence in endoscopy: a clinician's guide.
Expert review of gastroenterology & hepatology
Jun 17, 2020
INTRODUCTION: Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research and development (R &D) is being actively conducted. With the...
Deep Learning
Rectal Diseases
Endoscopy, Gastrointestinal
Helicobacter Infections
Humans
Stomach Neoplasms
Colorectal Neoplasms
Image Interpretation, Computer-Assisted
Helicobacter pylori
Capsule Endoscopy
Gastrointestinal Neoplasms
Precancerous Conditions
Esophageal Neoplasms
Pharyngeal Neoplasms
Colonic Polyps
A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jun 12, 2020
BACKGROUND AND PURPOSE: Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for O...
Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software.
Acta radiologica (Stockholm, Sweden : 1987)
Jun 9, 2020
BACKGROUND: Good feature reproducibility enhances model reliability. The manual segmentation of gastric cancer with liver metastasis (GCLM) can be time-consuming and unstable.
Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis.
Methods (San Diego, Calif.)
May 19, 2020
Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-aided tissue examination using machine learning techniques, especially convolutional neural networks. A number of convolutional neural network-based methodolog...
A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Apr 30, 2020
BACKGROUND: Accurate delineation of cancer margins is critical for endoscopic curative resection. This study aimed to train and validate real-time fully convolutional networks for delineating the resection margin of early gastric cancer (EGC) under i...
Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study.
Annals of oncology : official journal of the European Society for Medical Oncology
Apr 15, 2020
BACKGROUND: Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough.
SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network.
Scientific reports
Mar 18, 2020
Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-disease associations identified by high-throughput technologies is increasing, the experimental methods ...
Hierarchical Analysis of Factors Associated with T Staging of Gastric Cancer by Endoscopic Ultrasound.
Digestive diseases and sciences
Mar 17, 2020
BACKGROUND: Size, ulcer, differentiation, and location are known to be factors affecting the T stage accuracy of EUS in gastric cancer. However, whether an interaction exists among recognized variables is poorly understood. The aim of this study was ...
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