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NCT Number: NCT06393153

Model for Prognosis of Elderly Gastric Cancer Patients

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.

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Key information

Conditions

Stomach Neoplasms Aged Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Neoplasms Neoplasms by Site Stomach Diseases

Age range

75 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Gastric Surgery， Fujian Medical University Union Hospital

Fuzhou, Fujian, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. 5-Year Overall Survival

    Time frame: 5 years or 60 months.

    The proportion of patients who are alive at five years after treatment for elderly gastric cancer patients.

  2. 5-Year Disease-Free Survival

    Time frame: 5 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 outcomes

  1. 3-Year Overall Survival

    Time frame: 3 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.

  2. 1-Year Overall Survival

    Time frame: 1 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. 3-Year Disease-Free Survival

    Time frame: 3 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.

  4. 1-Year Disease-Free Survival

    Time frame: 1 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.

Sponsors and collaborators

Lead sponsor

Chang-Ming Huang, Prof.

Other

Registry information

Official study title

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

Important dates

Study start
2024
Primary completion
2024
Study completion
2024
First posted
May 1, 2024
Registry last updated
May 1, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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