Clinical trial · Observational
Predict 5-Year Survival in Elderly Gastric Cancer
Machine Learning Models to Predict 5-Year Post-Surgery Survival of Older Gastric Cancer Patients
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
In this study, elderly patients with gastric cancer who underwent radical gastrectomy in Union Hospital Affiliated to Fujian Medical University from 2012 to 2018 were included as a derived cohort, and the training set and internal validation set were randomly divided by 4:1. Machine learning strategies of random forest, decision tree and support vector machine are used to construct survival prediction model. Each model was tested in an internal validation set and an external validation set consisting of patients from two other large medical centers.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Gastric Cancer | Malignant Gastric Neoplasm | CURATED_BROADER | 0.80 |
| Machine Learning | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- 5-year overall survival
- timeFrame
- 5 years or 60 months.
- description
- Survival status at 5 years: survival, death, survival with tumor, deletion.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 65 Years
Show eligibility criteria text
Inclusion Criteria: * (1) GC diagnosis confirmed by abdominal computed tomography (CT) or biopsy; (2) age ≥65 years at diagnosis; (3) underwent radical surgical resection without evidence of distant metastasis; and (4) availability of complete clinical and pathological data. Exclusion Criteria: * (1) postoperative pathology confirming non-gastric primary tumors; (2) distant metastasis; (3) incomplete clinical data; and (4) other concurrent malignancies within five years.
References
Publications (1)
- DERIVEDZhang XQ, Huang ZN, Wu J, Zheng CY, Liu XD, Huang YQ, Chen QY, Li P, Xie JW, Zheng CH, Lin JX, Zhou YB, Huang CM. Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers. BMC Cancer. 2025 Feb 1;25(1):188. doi: 10.1186/s12885-025-13545-x. PMID 39893402