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  1. Home
  3. Impact Of Body Composition Parameters, Age, And Tumor Staging On Gastric Cancer Prognosis.
  1. Home
  3. Impact Of Body Composition Parameters, Age, And Tumor Staging On Gastric Cancer Prognosis.

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Impact of body composition parameters, age, and tumor staging on gastric cancer prognosis.

Wei Li1,2,3,4,5, Hai Zhu3,6, Hai-Zheng Dong2

  • 1Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University.

European Journal of Cancer Prevention : the Official Journal of the European Cancer Prevention Organisation (ECP)
|September 4, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models predict gastric cancer prognosis by combining body composition, age, and tumor staging. Key factors like muscle attenuation and skeletal muscle index significantly impact recurrence-free and overall survival.

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Area of Science:

  • Oncology
  • Medical Informatics

Background:

  • Gastric cancer prognosis research lacks integrated analysis of body composition, age, and tumor staging.
  • Machine learning offers a novel approach to predict gastric cancer outcomes.

Purpose of the Study:

  • To develop and validate machine learning models for predicting gastric cancer prognosis.
  • To identify key prognostic factors including body composition, age, and tumor stage.

Main Methods:

  • Analysis of 1,132 gastric cancer patients' preoperative data.
  • Application of Cox regression and machine learning models, including decision tree analysis.
  • Inclusion of body composition metrics (MA, SMI, VSR) and clinical parameters (age, TNM staging).

Main Results:

  • Multivariate analysis identified age (≥65 years), TNM staging, low MA, low SMI, and low VSR as significant prognostic factors.
  • Decision tree analysis delineated distinct patient subgroups with varying recurrence-free survival (RFS) and overall survival (OS) based on these factors.
  • Optimal prognosis was observed in subgroups with high MA and early TNM staging (I, II); poorest prognosis was linked to low MA, advanced TNM staging (II, III), low SMI, and older age (≥65 years).

Conclusions:

  • Cox regression and decision tree analyses effectively identified critical prognostic factors and risk subgroups in gastric cancer.
  • The integration of body composition, age, and tumor staging enhances prognostic accuracy.
  • These findings can improve clinical decision-making and intervention planning for gastric cancer patients.