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Model for Prognosis of Elderly Gastric Cancer Patients

Development and Validation of a Machine Learning Model for Predicting the Prognosis of Elderly Gastric Cancer Patients: A Multi-Center Study in China

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06393153
Enrollment
1202
Registered
2024-05-01
Start date
2024-01-01
Completion date
2024-04-20
Last updated
2024-05-01

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Aged, Stomach Neoplasms

Keywords

Stomach Neoplasms, Prognosis, Elderly, Prediction Model

Brief summary

This study aims to develop and validate a Random Survival Forest (RSF) model for predicting long-term survival in elderly patients following curative resection for gastric cancer. The study is a retrospective multi-center analysis involving patients aged 75 and above who underwent gastric resection from January 2009 to December 2018 at nine top-tier hospitals in China. An online prognostic tool is introduced to assist clinicians in predicting patient prognosis and customizing treatment and follow-up strategies.

Detailed description

This retrospective multi-center study focuses on the development and validation of a predictive model for elderly gastric cancer patients. Data were collected from 16,344 gastric cancer patients, with 1,202 elderly patients ultimately included after applying exclusion criteria. Patients were randomly divided into training and testing cohorts in a 7:3 ratio. The study was approved by the institutional review boards with a waiver of informed consent due to the use of anonymized secondary data. The analysis employs the Random Survival Forest (RSF) method, incorporating variable importance and minimal depth techniques to select key variables for predicting overall survival (OS) and disease-free survival (DFS). The study also implements rigorous data handling procedures, including multiple imputations for missing data. The development of an online prognostic tool based on the RSF model is part of the project, designed to provide real-time survival predictions through a user-friendly interface for clinical application.

Interventions

None listed

Sponsors

Chang-Ming Huang, Prof.
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
75 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Patients aged 75 years or older. * Histologically confirmed diagnosis of gastric cancer. * Patients who have undergone curative gastrectomy.

Exclusion criteria

* Patients with non-primary gastric malignancies. * Pathological confirmation of metastatic disease (M1). * Incomplete follow-up data or loss to follow-up.

Design outcomes

Primary

MeasureTime frameDescription
5-Year Overall Survival5 years or 60 months.The proportion of patients who are alive at five years after treatment for elderly gastric cancer patients.
5-Year Disease-Free Survival5 years or 60 months.The proportion of patients who have survived without any signs or symptoms of gastric cancer for five years after the initial treatment.

Secondary

MeasureTime frameDescription
3-Year Overall Survival3 years or 36 months.The proportion of patients who have survived without any signs or symptoms of gastric cancer for 3 years after the initial treatment.
1-Year Overall Survival1 years or 12 months.The proportion of patients who have survived without any signs or symptoms of gastric cancer for 1 years after the initial treatment.
3-Year Disease-Free Survival3 years or 36 months.The proportion of patients who have survived without any signs or symptoms of gastric cancer for 3 years after the initial treatment.
1-Year Disease-Free Survival1 years or 12 months.The proportion of patients who have survived without any signs or symptoms of gastric cancer for 1 years after the initial treatment.

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026