Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.

Journal: PloS one
Published Date: Jul 24, 2024

Abstract

BACKGROUND: This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data.

Authors

  • Mohammad Moharrami
    Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.
  • Parnia Azimian Zavareh
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland.
  • Erin Watson
    Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.
  • Sonica Singhal
    Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.
  • Alistair E W Johnson
  • Ali Hosni
    Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Canada.
  • Carlos Quinonez
    Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.
  • Michael Glogauer
    Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.

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