In Radiography (London, England : 1995)
INTRODUCTION : The aim of this review is to describe how various AI-supported applications are used in head and neck cancer radiotherapy treatment planning, and the impact on dose management in regards to target volume and nearby organs at risk (OARs).
METHODS : Literature searches were conducted in databases and publisher portals Pubmed, Science Direct, CINAHL, Ovid, and ProQuest to peer reviewed studies published between 2015 and 2021.
RESULTS : Out of 464 potential ones, ten articles covering the topic were selected. The benefit of using deep learning-based methods to automatically segment OARs is that it makes the process more efficient producing clinically acceptable OAR doses. In some cases automated treatment planning systems can outperform traditional systems in dose prediction.
CONCLUSIONS : Based on the selected articles, in general AI-based systems produced time savings. Also, AI-based solutions perform at the same level or better than traditional planning systems considering auto-segmentation, treatment planning and dose prediction. However, their clinical implementation into routine standard of care should be carefully validated IMPLICATIONS TO PRACTICE: AI has a primary benefit in reducing treatment planning time and improving plan quality allowing dose reduction to the OARs thereby enhancing patients' quality of life. It has a secondary benefit of reducing radiation therapists' time spent annotating thereby saving their time for e.g. patient encounters.
Ahervo H, Korhonen J, Lim Wei Ming S, Guan Yunqing F, Soini M, Lian Pei Ling C, Metsälä E
2023-Mar-06
Artificial intelligence, Head and neck cancer, Radiotherapy, Treatment planning
