AIMC Topic: Stomach Neoplasms
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Robotic D2 Total Gastrectomy with Fluorescent Lymphatic Mapping for Gastric Cancer: Effective Use of the 4th Arm.
Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
Mar 16, 2021
Minimally invasive surgery techniques have evolved remarkably over the past few decades in the field of surgical oncology, including robotic techniques for gastric malignancies. Bedside surgical assistance is often limited by operating table space or...
Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning.
Nature communications
Mar 12, 2021
N-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the ...
Artificial intelligence in the upper GI tract: the future is fast approaching.
Gastrointestinal endoscopy
Mar 11, 2021
Micro-morphological feature visualization, auto-classification, and evolution quantitative analysis of tumors by using SR-PCT.
Cancer medicine
Mar 7, 2021
Tissue micro-morphological abnormalities and interrelated quantitative data can provide immediate evidences for tumorigenesis and metastasis in microenvironment. However, the multiscale three-dimensional nondestructive pathological visualization, mea...
Tumor Microenvironment
Liver Neoplasms
Liver
Principal Component Analysis
X-Ray Microtomography
Humans
Machine Learning
Synchrotrons
Neural Networks, Computer
Tomography, X-Ray Computed
Microvessels
Area Under Curve
Stomach Neoplasms
Intestinal Neoplasms
Neovascularization, Pathologic
Specimen Handling
Robotic proximal subtotal gastrectomy with double-tract reconstruction for gastric cancer: A video vignette.
Asian journal of surgery
Feb 9, 2021
Use of Artificial Intelligence in the Prediction of Malignant Potential of Gastric Gastrointestinal Stromal Tumors.
Digestive diseases and sciences
Feb 6, 2021
BACKGROUND AND AIMS: This study aimed to investigate whether AI via a deep learning algorithm using endoscopic ultrasonography (EUS) images could predict the malignant potential of gastric gastrointestinal stromal tumors (GISTs).
Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Jan 23, 2021
BACKGROUND: Traditional diagnosis methods for lymph node metastases are labor-intensive and time-consuming. As a result, diagnostic systems based on deep learning (DL) algorithms have become a hot topic. However, current research lacks testing with s...
Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer.
Scientific reports
Jan 14, 2021
To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poorly differentiated-type intramucosal gastric cancer. 1169 patients with postoperative gastric cancer were divided into a training group and a test grou...
Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning.
JAMA network open
Jan 4, 2021
IMPORTANCE: Occult peritoneal metastasis frequently occurs in patients with advanced gastric cancer and is poorly diagnosed with currently available tools. Because the presence of peritoneal metastasis precludes the possibility of curative surgery, t...
Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics.
PloS one
Dec 31, 2020
BACKGROUND: The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.
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