Ignite Creation Date:
2024-05-06 @ 8:28 PM
Last Modification Date:
2024-10-26 @ 3:28 PM
Study NCT ID:
NCT06393153
Status:
COMPLETED
Last Update Posted:
2024-05-01
First Post:
2024-04-26
Brief Title:
Model for Prognosis of Elderly Gastric Cancer Patients
Sponsor:
Chang-Ming Huang Prof
Organization:
Fujian Medical University
Study Overview
Official Title:
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
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 16344 gastric cancer patients with 1202 elderly patients ultimately included after applying exclusion criteria Patients were randomly divided into training and testing cohorts in a 73 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
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None