Recurrence and Prognosis Prediction Model for Gastric Cancer

Fudan University

Status: Completed

Conditions: Gastric Cancer (GC)

Treatments: Other: surgery and/or chemo

Study type: Observational

Funder types: Other

Identifiers

NCT07243847

Prediction model(GC)

Details and patient eligibility

About

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.

Enrollment: 5,000 patients

Sex: All

Volunteers: No Healthy Volunteers

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.