Impact of harmonization on predicting complications in head and neck cancer after radiotherapy using MRI radiomics and machine learning techniques.

Journal: Medical physics
Published Date: Mar 31, 2025

Abstract

BACKGROUND: Variations in medical images specific to individual scanners restrict the use of radiomics in both clinical practice and research. To create reproducible and generalizable radiomics-based models for outcome prediction and assessment, data harmonization is essential.

Authors

  • 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.
  • Amin Talebi
    Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • Seid Rabi Mahdavi
    Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • Beth Ghavidel
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
  • Farshid Arbabi Kalati
    Department of Radiation Oncology, Roshana Cancer Institute, Tehran, Iran.
  • Seyed Hadi Molana
    Department of Radiation Oncology, Aja University of Medical Sciences, Tehran, Iran.
  • Yang Lei
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Meysam Tavakoli
    Department of Radiation Onc, ology, and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.

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