Gastric Cancer, Mechine Learning, Precancerous Lesion
Conditions
Brief summary
Based on the gastric cancer database established earlier, this project explored the PG standard suitable for Chinese people, and further explored the establishment of machine learning model to stratify gastric cancer risk in the population, guide the frequency of gastroscopy screening, and extract important gastric cancer risk factors from it.Establish electronic health records of gastric organs, track the development and outcome of gastric diseases through deep learning method, in order to predict the development and outcome of gastric diseases;Then, the simulation hypothesis deductive method is used to compare the outcomes that may be caused by different lifestyles with the help of deep learning model, so as to guide patients to develop a better lifestyle and explore the establishment of health management paths for gastric cancer patients and high-risk groups in China.
Interventions
OTHERpepsinogen
diagnostic value of pepsinogen for severe atrophy and gastric cancer
Sponsors
Second Affiliated Hospital, School of Medicine, Zhejiang University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
25 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* 1） intention to undergo gastroscopy during health checkup examination; and 2) 25-75 years of age
Exclusion criteria
* 1\) a history of gastric ulcer, gastric polyp, or GC; 2) a history of gastrectomy; 3) treatment with a proton pump inhibitor in the last month; 4) contraindications to gastroscopy; 5) a history of Hp eradication; 6) a history of abdominal pain, abdominal distention, belching, acid reflux, nausea and other digestive tract symptoms within 1 month or 67) incomplete data.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| pepsinogen value for precanceous lesion and Gastric cancer | 1 year | pepsinogen value for precanceous lesion and Gastric cancer |
Countries
China
Contacts
Primary ContactYuling Tong, Dr.
Backup ContactYi Zhao, Master
Outcome results
None listed