Using AI for Brain Tumour Treatment
Radiation therapy (RT) is a common treatment for brain tumours (the second most common cancer in childhood after leukemia). The goal is to deliver a focused dose of radiation to the tumour and surrounding at-risk regions while minimizing radiation to normal brain tissues. This is particularly crucial for young patients in whom excess RT to normal brain tissues can lead to significant long-term side effects.
The standard method of manual radiation therapy planning is time-consuming and can vary in quality depending on the experience of the radiation therapist and oncology staff. Artificial intelligence (AI)-assisted planning has been shown to improve the speed and quality of RT plan design and reduce unnecessary doses to normal brain tissues.
In a project involving the Princess Margaret Cancer Centre in Toronto, as well as the London Regional Cancer Program (Ontario), researchers evaluated the potential benefit of collaborative AI-RT planning for children, youth and young adults with brain tumours. The group intends to disseminate the cutting-edge AI technology and create new collaborations to improve RT for thousands of cancer patients across Canada.
For this study, 61 patients were enrolled, with approximately two RT plans created by expert RT planners (standard practice) and one AI-assisted plan created for each patient. In a blind evaluation by the treating oncologists, the AI-assisted plans were deemed to be as good or better than the manual plans. AI-assisted plans delivered a slightly smaller dose of radiation to normal brain tissues and were designed in less time.
The conclusion: AI-assisted radiation therapy planning creates high-quality radiotherapy plans for children and young adults with brain tumours. This is the first study of AI-assisted RT planning to include children, and creates an opportunity to rapidly create high quality plans for patients needing urgent treatment, and facilitate planning for smaller centres.
Read the full published article: A Prospective Study of Machine Learning − Assisted Radiation Therapy Planning for Patients Receiving 54 Gy to the Brain
Derek S. Tsang, MD, MSc; Grace Tsui, BSc, MRT(T); Anna T. Santiago, MSc, MPH; Harald Keller, PhD; Thomas Purdie, PhD, FAAPM; Chris Mcintosh, PhD; Glenn Bauman, MD; Nancy La Macchia, MRT(T); Amy Parent, BSc, MRT(T); Hitesh Dama, BSc, MRT(T); Sameera Ahmed, MSc; Normand Laperriere, MD; Barbara-Ann Millar, MBChB; Valerie Liu, BSc; David C. Hodgson, MD, MPH, FASTRO
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