The Research of Constructing a Risk Assessment Model for Gastric Cancer Based on Machine Learning

NCT04957407 | Unknown

• 1 other identifier: 71804161
• Study Type: observational
• Target: 5,000
• Locations: 1 country
• Sites: 2

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.

Conditions

Gastric Cancer; Precancerous Lesion; Mechine Learning

Outcome Measures

Primary Outcomes (1)

• pepsinogen value for precanceous lesion and Gastric cancer

  pepsinogen value for precanceous lesion and Gastric cancer

  1 year

Study Arms (4)

non-atrophic gastritis

OLGA-0 group；OLGA (Operative Link on Gastritis Assessment)

mild-moderate atrophic gastritis

OLGA I-II group；OLGA (Operative Link on Gastritis Assessment)

severe atrophic gastritis

OLGA III-IV group；OLGA (Operative Link on Gastritis Assessment)

Other: pepsinogen

gastric cancer

gastric cancer

Other: pepsinogen

Interventions

pepsinogen OTHER

diagnostic value of pepsinogen for severe atrophy and gastric cancer

gastric cancer; severe atrophic gastritis

Eligibility Criteria

• Age: 25 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

consecutive subjects who underwent regular health checkup at nine International Healthcare

You may qualify if:

• ） intention to undergo gastroscopy during health checkup examination; and 2) 25-75 years of age

You may not qualify if:

• \) 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.

Sponsors & Collaborators

• Second Affiliated Hospital, School of Medicine, Zhejiang University: lead

Study Sites (2)

Zhejiang Provincial Hospital of Traditional Chinese Medicine

Hangzhou, China

RECRUITING

Ningbo cadres health center

Ningbo, China

RECRUITING

MeSH Terms

Conditions

Stomach Neoplasms

Interventions

Pepsinogen A

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases

Intervention Hierarchy (Ancestors)

Pepsinogens; Enzyme Precursors; Enzymes and Coenzymes; Protein Precursors; Proteins; Amino Acids, Peptides, and Proteins

Study Officials

• Yuling Tong, Dr.

  2nd affiliated hospital of Zhejiang University, school of medicine

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Yuling Tong, Dr.

CONTACT

1375821220; tongyl0313@zju.edu.cn

Yi Zhao, Master

CONTACT

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 8, 2021
• First Posted: July 12, 2021
• Study Start: January 1, 2019
• Primary Completion: May 31, 2022
• Study Completion: December 31, 2022
• Last Updated: July 12, 2021

Record last verified: 2021-06

Locations

CN (2)