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Machine learning applications in radiation oncology

Journal Article


Abstract


  • Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.

UOW Authors


  •   Field, Matthew (external author)
  •   Hardcastle, Nicholas (external author)
  •   Holloway, Lois (external author)
  •   Jameson, Michael (external author)

Publication Date


  • 2021

Citation


  • Field, M., Hardcastle, N., Jameson, M., Aherne, N., & Holloway, L. (2021). Machine learning applications in radiation oncology. Physics and Imaging in Radiation Oncology, 19, 13-24. doi:10.1016/j.phro.2021.05.007

Scopus Eid


  • 2-s2.0-85108591085

Start Page


  • 13

End Page


  • 24

Volume


  • 19

Issue


Place Of Publication


Abstract


  • Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.

UOW Authors


  •   Field, Matthew (external author)
  •   Hardcastle, Nicholas (external author)
  •   Holloway, Lois (external author)
  •   Jameson, Michael (external author)

Publication Date


  • 2021

Citation


  • Field, M., Hardcastle, N., Jameson, M., Aherne, N., & Holloway, L. (2021). Machine learning applications in radiation oncology. Physics and Imaging in Radiation Oncology, 19, 13-24. doi:10.1016/j.phro.2021.05.007

Scopus Eid


  • 2-s2.0-85108591085

Start Page


  • 13

End Page


  • 24

Volume


  • 19

Issue


Place Of Publication