Trials / Completed
CompletedNCT07243847
Recurrence and Prognosis Prediction Model for Gastric Cancer
Artificial Deep Learning-Based Model for Predicting Postoperative Recurrence in Gastric Cancer
- Status
- Completed
- Phase
- —
- Study type
- Observational
- Enrollment
- 5,000 (actual)
- Sponsor
- Fudan University · Academic / Other
- Sex
- All
- Age
- —
- Healthy volunteers
- Not accepted
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
Interventions
| Type | Name | Description |
|---|---|---|
| OTHER | surgery and/or chemo | Deep learning model |
Timeline
- Start date
- 2000-01-01
- Primary completion
- 2025-10-01
- Completion
- 2025-11-01
- First posted
- 2025-11-24
- Last updated
- 2025-11-24
Source: ClinicalTrials.gov record NCT07243847. Inclusion in this directory is not an endorsement.