Gastric Cancer (GC)
Conditions
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.
Interventions
OTHERsurgery and/or chemo
Deep learning model
Sponsors
Fudan University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| recurrence | 3 year after surgery |
Outcome results
None listed