Model for Prognosis of Elderly Gastric Cancer Patients

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

1202 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-01-01

Study Completion Date

2024-04-20

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Predict 5-Year Survival in Elderly Gastric Cancer

NCT06208046

Gastric Cancer Machine Learning

COMPLETED

Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients

NCT06548464

Stomach Neoplasms Gastrectomy Machine Learning

COMPLETED

Prediction Model for Advanced Gastric Cancer in Elderly

NCT06467591

Stomach Neoplasms Aged

COMPLETED

In-hospital Outcomes of Older Patients With Gastric Cancer and Their Risk Factors

NCT06185907

Gastric Cancer

COMPLETED

Multi-center and Multi-modal Deep Learning Study of Gastric Cancer

NCT05001321

Stomach Neoplasms

UNKNOWN

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Stomach Neoplasms Aged

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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.

Minimum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No