User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

Mattea L. Welch*, Chris McIntosh, Andrea McNiven, Shao Hui Huang, Bei-Bei Zhang, Leonard Wee, Alberto Traverso, Brian O'Sullivan, Frank Hoebers, Andre Dekker, David A. Jaffray

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Original languageEnglish
Pages (from-to)145-152
Number of pages8
JournalPhysica Medica: European journal of medical physics
Volume70
DOIs
Publication statusPublished - Feb 2020
EventInternational Conference on the Use of Computers in Radiation Therapy (ICCR) / International Conference on Monte Carlo Techniques for Medical Applications (MCMA) - Montreal, Canada
Duration: 17 Jun 201921 Jun 2019

Keywords

  • Head and neck
  • Outcome prediction
  • Deep learning
  • Machine learning
  • User-controlled
  • Bias
  • PROGNOSTIC-FACTORS
  • RADIOTHERAPY
  • CANCER
  • RADIOMICS
  • CARCINOMA
  • SURVIVAL
  • IMPACT
  • RISK

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