Artificial intelligence, system analysis and simulation modeling in prediction of gastric cancer patients survivial after complete combined gastrectomies

2005 ◽  
Vol 23 (16_suppl) ◽  
pp. 4246-4246
Author(s):  
O. Kshivets
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Cancer Patients ◽  
Simulation Modeling ◽  
System Analysis ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Gastric Cancer Patients

scholarly journals B-PO03-101 ABLATION DELIVERY PARAMETERS PREDICT IMPEDANCE DECREASE DURING ATRIAL FIBRILLATION ABLATION VIA A CLOUD ARTIFICIAL INTELLIGENCE SYSTEM ANALYSIS

Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S229-S230
Author(s):  
Clinton J. Thurber ◽  
John Whitaker ◽  
Omar Kreidieh ◽  
Ahmad Halawa ◽  
Parinita A. Dherange ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
System Analysis ◽  
Atrial Fibrillation Ablation ◽  
Artificial Intelligence System ◽  
Intelligence System

scholarly journals Analysis of Extracellular Vesicles in Gastric Juice from Gastric Cancer Patients

2019 ◽  
Vol 20 (4) ◽  
pp. 953 ◽  
Author(s):  
Shuji Kagota ◽  
Kohei Taniguchi ◽  
Sang-Woong Lee ◽  
Yuko Ito ◽  
Yuki Kuranaga ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Gastric Juice ◽  
Cancer Patients ◽  
Extracellular Vesicles ◽  
System Analysis ◽  
Tracking System ◽  
Immune Surveillance ◽  
Membrane Vesicles ◽  
Normal Fibroblast ◽  
Gastric Cancer Patients

Extracellular vesicles (EVs) are secretory membrane vesicles containing lipids, proteins, and nucleic acids; they function in intercellular transport by delivering their components to recipient cells. EVs are observed in various body fluids, i.e., blood, saliva, urine, amniotic fluid, and ascites. EVs secreted from cancer cells play important roles in the formation of their environment, including fibrosis, angiogenesis, evasion of immune surveillance, and even metastasis. However, EVs in gastric juice (GJ-EVs) have been largely unexplored. In this study, we sought to clarify the existence of GJ-EVs derived from gastric cancer patients. GJ-EVs were isolated by the ultracentrifuge method combined with our own preprocessing from gastric cancer (GC) patients. We verified GJ-EVs by morphological experiments, i.e., nanoparticle tracking system analysis and electron microscopy. In addition, protein and microRNA markers of EVs were examined by Western blotting analysis, Bioanalyzer, or quantitative reverse transcription polymerase chain reaction. GJ-EVs were found to promote the proliferation of normal fibroblast cells. Our findings suggest that isolates from the GJ of GC patients contain EVs and imply that GJ-EVs partially affect their microenvironments and that analysis using GJ-EVs from GC patients will help to clarify the pathophysiology of GC.

scholarly journals Evaluating the Effects of An Artificial Intelligence System on Endoscopy Quality and Preliminarily Testing its Performance on Detecting Early Gastric Cancer: A Randomized Controlled Trial

Endoscopy ◽  
2021 ◽  
Author(s):  
Lianlian Wu ◽  
Xinqi He ◽  
Mei Liu ◽  
Huaping Xie ◽  
Ping An ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Randomized Controlled Trial ◽  
Early Gastric Cancer ◽  
Controlled Trial ◽  
Inspection Time ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Randomized Controlled ◽  
Blind Spots

Background and study aims: Qualified esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). Our previous report showed that artificial intelligence system could monitor blind spots during EGD. Here, we updated the system to a new one (named ENDOANGEL), verified its effectiveness on improving endoscopy quality and pre-tested its performance on detecting EGC in a multi-center randomized controlled trial. Patients and methods: ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD examination in 5 hospitals were randomly assigned to ENDOANGEL-assisted (EA) group or normal control (NC) group. The primary outcome was the number of blind spots. The second outcome includes performance of ENDOANGEL on predicting EGC in clinical setting. Results: 1,050 patients were recruited and randomized. 498 and 504 patients in EA and NC groups were respectively analyzed. Compared with NC, the number of blind spots was less (5.382±4.315 vs. 9.821±4.978, p<0.001) and the inspection time was prolonged (5.400±3.821 min vs. 4.379±3.907 min, p<0.001) in EA group. In the 498 patients from EA group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all 3 EGC (1 mucosal carcinoma and 2 high-grade neoplasia) and 2 advanced gastric cancer, with a per-lesion accuracy of 84.69%, sensitivity of 100% and specificity of 84.29% for detecting GC. Conclusions: The results of the multi-center study confirmed that ENDOANGEL is an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

Relationship between Topoisomerase II Alpha Overexpression and Prognosis in Chinese Gastric Cancer Patients

2017 ◽  
Vol 36 (3) ◽  
pp. 207-216 ◽  
Author(s):  
Fangchao Zheng ◽  
Jiuda Zhao ◽  
Feng Du ◽  
Junhui Zhao ◽  
Guoshuang Shen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Topoisomerase Ii ◽  
Topoisomerase Ii Alpha ◽  
Gastric Cancer Patients
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