Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study.

Journal: Technology in cancer research & treatment
PMID: 33969761

Abstract

BACKGROUND: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient and tumor characteristics, dosimetric parameters, and treatment features are intertwined, and it is not always possible to associate RP with a single parameter. This study aimed to determine the algorithm that most accurately predicted RP development with machine learning.

Authors

  • Melek Yakar
    Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, Turkey.
  • Durmus Etiz
    Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, Turkey.
  • Muzaffer Metintas
    Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.
  • Guntulu Ak
    Department of Chest Diseases, Medical Faculty of Osmangazi University, Eskişehir, Turkey.
  • Özer Çelik
    Department of Mathematics and Computer, Faculty of Science and Letters, Eskişehir Osmangazi University, Eskişehir, Turkey.

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