Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.

Journal: Clinical radiology
PMID: 33941364

Abstract

AIM: To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma.

Authors

  • M Patel
    University of Birmingham, Birmingham, UK; Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • J Zhan
    Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; The Affiliated Hospital of Qingdao University, Qingdao Shi, Shandong Sheng, China.
  • K Natarajan
    Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • R Flintham
    Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • N Davies
    Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • P Sanghera
    University of Birmingham, Birmingham, UK; Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • J Grist
    University of Birmingham, Birmingham, UK.
  • V Duddalwar
    Departments of Radiology, Urology and Biomedical Engineering, University of Southern California, USA.
  • A Peet
    University of Birmingham, Birmingham, UK; Birmingham Children's Hospital, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK.
  • V Sawlani
    University of Birmingham, Birmingham, UK; Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK. Electronic address: Vijay.Sawlani@uhb.nhs.uk.

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