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Predicting radiation-acute esophagitis via machine learning algorithms.

Mostafa Alizade-Harakiyan1, Amin Khodaei2, Hamed Zamani3

  • 1Department of Radiation Oncology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran; Medical Physics Department, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran; Molecular Medicine Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.

Computational Biology and Chemistry
|July 8, 2025
PubMed
Summary

Machine learning accurately predicts acute esophagitis from radiochemotherapy using dose-volume data. This approach enhances patient management by identifying high-risk individuals early, achieving over 90% accuracy.

Keywords:
Acute esophagitisArtificial IntelligenceMachine LearningRadio-Chemotherapy

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Area of Science:

  • Oncology
  • Radiotherapy
  • Machine Learning

Background:

  • Acute esophagitis is a common side effect of cancer radiochemotherapy.
  • Early detection and prediction of esophagitis are crucial for patient management and treatment planning.

Purpose of the Study:

  • To compare Machine Learning (ML) algorithms for predicting acute esophagitis.
  • To explore practical implementations of ML in clinical settings for esophagitis prediction.

Main Methods:

  • Collected and preprocessed a dataset of patient characteristics, treatment parameters, and clinical factors.
  • Trained and validated ML classification algorithms using cross-validation techniques.
  • Explored real-world integration of ML models into clinical practice.

Main Results:

  • Dose-volume characteristics were key predictors of esophagitis, outperforming other factors.
  • ML algorithms achieved over 90% F1-scores and accuracies in predicting esophagitis grades.
  • Area Under the Curve (AUC) exceeded 95% for distinguishing high-grade esophagitis.

Conclusions:

  • ML shows significant potential for improving patient outcomes in cancer treatment.
  • Dose-volume characteristics are critical for accurate prediction of acute esophagitis.