Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy

Highlights • Machine learning-based NTCP modelling of acute dysphagia was performed.• The models generated performed well on internal and external validation.• Doses of approximately 1 Gy/fraction were most strongly associated with severe dysphagia.• No spatial variation in radiosensitivity was observed for the pharyngeal mucosa.• These results could inform clinical decision-support and radiotherapy planning.

• Publication year

  2017

• Venue

  Clinical and Translational Radiation Oncology

• Publication date

  2017-11-21

• Fields of study

  Medicine, Engineering

• Identifiers
  DOI 10.1016/j.ctro.2017.11.009PMID 29399642PMCID 5796681
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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