A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters.

Journal: European journal of radiology
Published Date: Jan 1, 2019

Abstract

PURPOSE: To propose a computer-assisted method for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted magnetic resonance imaging (PWI).

Authors

  • Mahrooz Malek
    Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, Iran.
  • Masoumeh Gity
    Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Azadeh Alidoosti
    Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, Iran. Electronic address: azadeh.alidousti@gmail.com.
  • Zeinab Oghabian
    Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
  • Pariya Rahimifar
    Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, Iran.
  • Seyede Mahdieh Seyed Ebrahimi
    Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, Iran.
  • Elnaz Tabibian
    Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, Iran.
  • Mohammad Ali Oghabian
    Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran.

Keywords

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