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Recurrence and Prognosis Prediction Model for Gastric Cancer

NCT07243847 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 5000

Last updated 2025-11-24

No results posted yet for this study

Summary

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.

Conditions

  • Gastric Cancer (GC)

Interventions

OTHER

surgery and/or chemo

Deep learning model

Sponsors & Collaborators

  • Fudan University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2000-01-01
Primary Completion
2025-10-01
Completion
2025-11-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07243847 on ClinicalTrials.gov