Recurrence and Prognosis Prediction Model for Gastric Cancer

November 20, 2025 updated by: Jun Lu, Fudan University

Artificial Deep Learning-Based Model for Predicting Postoperative Recurrence in Gastric Cancer

This study, utilizing a large-scale multicenter Eastern database, has established a Deep Learning-based predictive model for recurrence following gastric cancer surgery, which demonstrates robust discriminatory power for early recurrence. Furthermore, the individualized recurrence probability generated by this model can predict long-term postoperative prognosis and effectively stratify patients based on risk, thereby guiding personalized treatment choices. This individualized risk probability is also applicable to both adjuvant chemotherapy and neoadjuvant chemotherapy populations, offering valuable support for precision treatment in gastric cancer.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

5000

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

A retrospective analysis was conducted on the clinicopathological data of patients who underwent radical gastrectomy for gastric cancer between 2001 and 2022 at 13 tertiary hospitals in China.

Description

Inclusion Criteria:

Pathologically confirmed gastric adenocarcinoma; No distant metastases confirmed by preoperative examinations such as chest X-ray, abdominal ultrasonography, and upper abdominal computed tomography; Achievement of R0 resection.

Exclusion Criteria:

Presence of distant metastases detected preoperatively or intraoperatively; Prior neoadjuvant chemotherapy or radiotherapy; Incomplete general clinical data.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
recurrence
Time Frame: 3 year after surgery
3 year after surgery

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fudan University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2000

Primary Completion (Actual)

October 1, 2025

Study Completion (Actual)

November 1, 2025

Study Registration Dates

First Submitted

November 16, 2025

First Submitted That Met QC Criteria

November 20, 2025

First Posted (Actual)

November 24, 2025

Study Record Updates

Last Update Posted (Actual)

November 24, 2025

Last Update Submitted That Met QC Criteria

November 20, 2025

Last Verified

November 1, 2025

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Prediction model(GC)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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