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Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis.

Mostafa Alizade-Harakiyan1, Amin Khodaei2, Ali Yousefi3

  • 1Department of Radiation Oncology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.

Biochemistry and Biophysics Reports
|April 15, 2025
PubMed
Summary

A new decision tree model accurately predicts radiation-induced esophagitis in cancer patients. This tool aids in optimizing radiotherapy and personalizing patient risk assessment for better outcomes.

Keywords:
Decision tree classifierMachine learningPredictive modelingRadiation-induced esophagitisRadiotherapyTreatment planning

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Area of Science:

  • Radiation Oncology
  • Medical Informatics
  • Clinical Decision Support

Background:

  • Radiation-induced esophagitis is a significant challenge in thoracic and neck cancer treatment.
  • It negatively impacts patient quality of life and can limit therapeutic efficacy.
  • Predicting and managing esophagitis is crucial for effective chemoradiotherapy.

Purpose of the Study:

  • To develop and validate a decision tree-based model for predicting acute esophagitis grades.
  • To identify key clinical and dosimetric predictors of radiation-induced esophagitis.
  • To provide a tool for personalized risk assessment in radiotherapy planning.

Main Methods:

  • Analysis of data from 100 patients undergoing thoracic and neck radiotherapy.
  • Utilized 33 features including demographic, clinical, and dosimetric parameters.
  • Implemented a decision tree classifier for binary (Grade ≥2 vs. <2) and multi-class (Grades 1, 2, 3) classification.

Main Results:

  • The binary classification model achieved 97% accuracy in predicting esophagitis.
  • The multi-class model demonstrated 98% accuracy in predicting specific esophagitis grades.
  • Key predictors identified include V40, V60 (volume receiving 40 Gy and 60 Gy, respectively), and average esophageal dose.

Conclusions:

  • The decision tree model accurately predicts radiation-induced esophagitis grades with high interpretability.
  • This approach facilitates treatment optimization and personalized risk assessment in radiation oncology.
  • The model serves as a promising tool for clinical decision support, enhancing radiotherapy planning.