Recurrence and Prognosis Prediction Model for Gastric Cancer

NCT ID: NCT07243847

Last Updated: 2025-11-24

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 5000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2000-01-01
• Study Completion Date: 2025-11-01

Brief 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)

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

surgery and/or chemo

Deep learning model

Intervention Type: OTHER

Eligibility Criteria

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.

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Fudan University

OTHER

Sponsor Role: lead

Responsible Party

Jun Lu

Clinical Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Other Identifiers

Prediction model(GC)

Identifier Type: -

Identifier Source: org_study_id