Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.

Journal: BMC neuroscience
PMID: 38789970

Abstract

INTRODUCTION: The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN).

Authors

  • Mohammad Amin Shahram
    Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Hosein Azimian
    Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Bita Abbasi
    Department of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Zohreh Ganji
    Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Zahra Khandan Khadem-Reza
    Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Elham Khakshour
    Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Hoda Zare
    Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

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