AIMC Topic: Stomach Neoplasms
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ASO Author Reflections: Applications of Artificial Intelligence in Oesophago-Gastric Malignancies-Present Work and Future Directions.
Annals of surgical oncology
Nov 18, 2021
Our paper highlights the use of artificial intelligence (AI) in oesophageal and gastric malignancies with acceptable levels of accuracy for both diagnostic and surveillance purposes. Here, we comment on the past, present and future work necessary for...
The viral expression and immune status in human cancers and insights into novel biomarkers of immunotherapy.
BMC cancer
Nov 5, 2021
BACKGROUND: Viral infections are prevalent in human cancers and they have great diagnostic and theranostic values in clinical practice. Recently, their potential of shaping the tumor immune microenvironment (TIME) has been related to the immunotherap...
DNA, Viral
Epstein-Barr Virus Infections
Herpesvirus 4, Human
Mutation
Stomach Neoplasms
Squamous Cell Carcinoma of Head and Neck
Tumor Microenvironment
Head and Neck Neoplasms
Humans
Biomarkers, Tumor
Transcriptome
Support Vector Machine
Machine Learning
Neoplasms
RNA-Seq
Tumor Virus Infections
Leukocytes
Immunotherapy
Genetic Variation
Kaplan-Meier Estimate
Papillomavirus Infections
High-Throughput Nucleotide Sequencing
Papillomaviridae
Genome, Viral
Oncogenic Viruses
Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.
EBioMedicine
Oct 19, 2021
BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.
Development and evaluation of a double-check support system using artificial intelligence in endoscopic screening for gastric cancer.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Oct 15, 2021
BACKGROUND: This study aimed to prevent missing gastric cancer and point out low-quality images by developing a double-check support system (DCSS) for esophagogastroduodenoscopy (EGD) still images using artificial intelligence.
The impact of robotic technology on the learning curve for robot-assisted gastrectomy in the initial clinical application stage.
Surgical endoscopy
Oct 7, 2021
OBJECTIVE: To evaluate the impact of robotic technology on the learning curve for robot-assisted gastrectomy in the initial clinical application stage and to compare RAG with laparoscopic-assisted gastrectomy using a short-term evaluation.
Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio.
Scientific reports
Sep 28, 2021
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
Sep 20, 2021
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
Sep 17, 2021
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
Sep 7, 2021
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
Aug 24, 2021
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...
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