Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT.

Journal: Clinical radiology
Published Date:

Abstract

AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas.

Authors

  • M M Elmohr
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • D Fuentes
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • M A Habra
    Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • P R Bhosale
    Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • A A Qayyum
    Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • E Gates
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • A I Morshid
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • J D Hazle
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • K M Elsayes
    Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: kmelsayes@mdanderson.org.