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  1. Home
  3. Predicting 3-year All-cause Mortality In Rectal Cancer Patients Based On Body Composition And Machine Learning.
  1. Home
  3. Predicting 3-year All-cause Mortality In Rectal Cancer Patients Based On Body Composition And Machine Learning.

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Predicting 3-year all-cause mortality in rectal cancer patients based on body composition and machine learning.

Xiangyong Li1, Zeyang Zhou1, Xiaoyang Zhang1

  • 1Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.

Frontiers in Nutrition
|March 18, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models predict 3-year mortality after rectal cancer surgery by analyzing body composition. The XGBoost model, using parameters like adipose tissue and muscle mass, showed the best predictive accuracy for patient outcomes.

Keywords:
machine learningnutritionpredictive modelprognosisrectal cancer

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Area of Science:

  • Oncology
  • Radiology
  • Data Science

Background:

  • Abdominal adipose tissue and muscle mass significantly impact rectal cancer prognosis.
  • Laparoscopic total mesorectal excision (LaTME) is a common surgical procedure for rectal cancer.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting 3-year all-cause mortality after LaTME.
  • To identify key body composition parameters influencing survival post-rectal cancer surgery.

Main Methods:

  • Collected preoperative CT scan data and clinical characteristics from 186 patients undergoing LaTME.
  • Developed seven ML models to predict 3-year survival, dividing patients into training and validation cohorts.
  • Utilized SHAP values to interpret model predictions and identify important variables.

Main Results:

  • The XGBoost model achieved the highest predictive performance in the validation cohort with an AUROC of 0.911.
  • Key predictors of mortality included subcutaneous adipose tissue index (SAI), visceral adipose tissue index (VAI), and skeletal muscle density (SMD).
  • Other significant factors were the visceral-to-subcutaneous adipose tissue ratio (VSR) and subcutaneous adipose tissue density (SAD).

Conclusions:

  • Machine learning models integrating body composition effectively predict all-cause mortality after LaTME.
  • The XGBoost model demonstrates superior performance in identifying patients at higher risk of mortality.
  • Body composition analysis is crucial for improving prognostic accuracy in rectal cancer patients.