Journal article
Machine learning applications in radiation oncology
Matthew Field, Nicholas Hardcastle, Michael Jameson, Noel Aherne, Lois Holloway
PHYSICS & IMAGING IN RADIATION ONCOLOGY | ELSEVIER | Published : 2021
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..
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Funding Acknowledgements
The authors declare the following financial interests/personal re-lationships which may be considered as potential competing interests: N Hardcastle receives funding through a Varian Medical Systems Collab-orative Research Grant for Kidney SABR. This grant includes compo-nents of machine learning as applied for treatment planning.