Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 27240717

Abstract

BACKGROUND AND PURPOSE: Severe acute mucositis commonly results from head and neck (chemo)radiotherapy. A predictive model of mucositis could guide clinical decision-making and inform treatment planning. We aimed to generate such a model using spatial dose metrics and machine learning.

Authors

  • Jamie A Dean
    Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK. Electronic address: jamie.dean@icr.ac.uk.
  • Kee H Wong
    The Royal Marsden NHS Foundation Trust, London, UK.
  • Liam C Welsh
    The Royal Marsden NHS Foundation Trust, London, UK.
  • Ann-Britt Jones
    Head and Neck Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW3 6JJ, UK.
  • Ulrike Schick
    The Royal Marsden NHS Foundation Trust, London, UK.
  • Kate L Newbold
    The Royal Marsden NHS Foundation Trust, London, UK; The Institute of Cancer Research, London, UK.
  • Shreerang A Bhide
    The Royal Marsden NHS Foundation Trust, London, UK; The Institute of Cancer Research, London, UK.
  • Kevin J Harrington
    The Royal Marsden NHS Foundation Trust, London, UK; The Institute of Cancer Research, London, UK.
  • Christopher M Nutting
    The Royal Marsden NHS Foundation Trust, London, UK; The Institute of Cancer Research, London, UK.
  • Sarah L Gulliford
    Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.

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