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NCT07243847

Recurrence and Prognosis Prediction Model for Gastric Cancer

Completed Last updated 24 November 2025
What this trial tests

trial testing surgery and/or chemo in Gastric Cancer (GC) in 5,000 participants. Completed in 1 November 2025.

Timeline
1 January 2000
Primary endpoint
1 October 2025
1 November 2025

Quick facts

Lead sponsorFudan University
StatusCompleted
Study typeOBSERVATIONAL
Enrollment5,000
Start date1 January 2000
Primary completion1 October 2025
Estimated completion1 November 2025

Drugs / interventions tested

Conditions studied

Sponsor

Fudan University

Who can join

Eligibility, any sex, with Gastric Cancer (GC). Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.

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Other recruiting trials for Gastric Cancer (GC)

Currently open trials in the same condition.

Other Fudan University trials

Trials by the same sponsor.

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Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07243847.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing