Novel optimization framework for real-time automated radiation therapy

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The World Health Organization estimates that over 8 million people die of cancer every year, around 70% of them in low and middle income countries. Radiation therapy (RT), a process whereby x-ray or particle beams are used to kill specific cells in cancer patients, is one of the most commonly used and cost effective ways to help treat cancer patients. It is estimated that over 50% of all cancer patients may benefit from receiving RT during the course of their treatment, either on its own or in combination with surgery, chemotherapy, hormonal therapy, or immunotherapy. However, the delivery of radiation therapy treatment plans is time consuming, involves cumbersome treatment planning systems (TPS), is expensive both in terms of personnel and infrastructure, and can be hampered by inaccurate computational models. This makes the delivery of high-quality and affordable treatment a challenging task globally, but also one which disproportionally affects low and middle income countries. Further, while the availability of RT centres in North America, Europe, Japan and Australia is generally adequate to cover current needs, similar coverage remains poor in Africa (34% of estimated need covered) and in the wider Asia-Pacific region (61%). As a consequence, the majority of the global population does not have sufficient access to appropriate cancer treatment. Unless addressed, this situation is expected to worsen further given that cancer incidence rates are projected to grow significantly in low and middle income countries over the next decade. As such, increasing the availability of high-quality cancer treatment, and of RT in particular, is recognized as a key global and societal challenge. Within this context, making RT more widely available, increasingly accurate, faster, and more cost effective, will play an important part in addressing this challenge.The delivery of high-quality radiation therapy relies on accurate treatment planning systems to create appropriate radiation treatment plans (TP) across a spectrum of different cancer types. Optimizing these TP can require considerable computational resources and is often personnel-intensive. In this project we will develop a fully-automated treatment planning system prototype based on advanced optimization techniques and remote supercomputing, thus addressing an important difficulty in deploying RT systems in challenging and remote environments. The system will make use of a range of cutting-edge computational and machine learning techniques to optimize the efficiency and robustness of treatment planning systems, with the view to reduce infrastructure and personnel costs in radiation therapy centres, and to provide more flexibility for use where expertise and large-scale computational resources may not be readily available.