Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.

Journal: Clinical radiology
PMID: 33036778

Abstract

AIM: To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) computed tomography (CT) predicts disease progression in patients with locally advanced larynx and hypopharynx squamous cell carcinoma (SCC) receiving (chemo)radiotherapy.

Authors

  • J Zhong
    Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK. Electronic address: jim.zhong@nhs.net.
  • R Frood
    Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • P Brown
    Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • H Nelstrop
    Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • R Prestwich
    Department of Clinical Oncology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • G McDermott
    Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • S Currie
    Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK; Radiotherapy Research Group, Leeds Institute of Medical Research, Faculty of Medicine & Health, University of Leeds, Leeds, UK.
  • S Vaidyanathan
    Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • A F Scarsbrook
    Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK; Radiotherapy Research Group, Leeds Institute of Medical Research, Faculty of Medicine & Health, University of Leeds, Leeds, UK.

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