Laurence Court, Ajay Aggarwal, Hester Burger, Carlos Cardenas, Christine Chung, Raphael Douglas, Monique du Toit, David Jaffray, Anuja Jhingran, Michael Mejia, Raymond Mumme, Sikudhani Muya, Komeela Naidoo, Jerry Ndumbalo, Kelly Nealon, Tucker Netherton, Callistus Nguyen, Niki Olanrewaju, Jeannette Parkes, Willie Shaw, Christoph Trauernicht, Melody Xu, Jinzhong Yang, Lifei Zhang, Hannah Simonds, Beth M. Beadle

Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant

  • Cancer Research
  • Oncology
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PURPOSE Automation, including the use of artificial intelligence, has been identified as a possible opportunity to help reduce the gap in access and quality for radiotherapy and other aspects of cancer care. The Radiation Planning Assistant (RPA) project was conceived in 2015 (and funded in 2016) to use automated contouring and treatment planning algorithms to support the efforts of oncologists in low- and middle-income countries, allowing them to scale their efforts and treat more patients safely and efficiently (to increase access). DESIGN In this review, we discuss the development of the RPA, with a particular focus on clinical acceptability and safety/risk across jurisdictions as these are important indicators for the successful future deployment of the RPA to increase radiotherapy availability and ameliorate global disparities in access to radiation oncology. RESULTS RPA tools will be offered through a webpage, where users can upload computed tomography data sets and download automatically generated contours and treatment plans. All interfaces have been designed to maximize ease of use and minimize risk. The current version of the RPA includes automated contouring and planning for head and neck cancer, cervical cancer, breast cancer, and metastases to the brain. CONCLUSION The RPA has been designed to bring high-quality treatment planning to more patients across the world, and it may encourage greater investment in treatment devices and other aspects of cancer treatment.

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