Gastric Cancer, Machine Learning
Conditions
Keywords
Gastric Cancer, Elderly, Oxidative Stress, Machine Learning, Overall Survival
Brief summary
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.
Detailed description
This is a retrospective, supervised learning, data mining study.
Interventions
None listed
Sponsors
Fujian Medical University Union Hospital
Fujian Medical University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 5-year overall survival | 5 years or 60 months. | Survival status at 5 years: survival, death, survival with tumor, deletion. |
Countries
China
Outcome results
None listed