Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.

Journal: Journal of applied clinical medical physics
PMID: 39704607

Abstract

PURPOSE: Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential advantages of treatment methods with the risks of harm to healthy tissues, including the heart. There is currently a lack of comprehensive, data-driven evidence on effective risk stratification strategies. The aim of this study is to investigate the prediction of cardiotoxicity using machine learning methods combined with radiomics, clinical, and dosimetric features.

Authors

  • Amin Talebi
    Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • Ahmad Bitarafan-Rajabi
    Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran; Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Azin Alizadeh-Asl
    Rajaei Cardiovascular Medical and Research Center, Cardio-Oncology Research Center, Iran University of Medical Science, Tehran, Iran.
  • Parisa Seilani
    Rajaei Cardiovascular Medical and Research Center, Cardio-Oncology Research Center, Iran University of Medical Science, Tehran, Iran.
  • Benyamin Khajetash
    Department of Medical physics, Iran University of Medical Sciences, Tehran, Iran.
  • Ghasem Hajianfar
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.
  • Meysam Tavakoli
    Department of Radiation Onc, ology, and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.

Keywords

External Resources

Popular Topics
Recent Journals