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

Author:

Court Laurence1ORCID,Aggarwal Ajay2ORCID,Burger Hester3ORCID,Cardenas Carlos4ORCID,Chung Christine1ORCID,Douglas Raphael1,du Toit Monique5,Jaffray David1ORCID,Jhingran Anuja1ORCID,Mejia Michael6,Mumme Raymond1ORCID,Muya Sikudhani7ORCID,Naidoo Komeela5ORCID,Ndumbalo Jerry7ORCID,Nealon Kelly1ORCID,Netherton Tucker1ORCID,Nguyen Callistus1ORCID,Olanrewaju Niki1ORCID,Parkes Jeannette3ORCID,Shaw Willie8ORCID,Trauernicht Christoph5ORCID,Xu Melody9ORCID,Yang Jinzhong1ORCID,Zhang Lifei1,Simonds Hannah10ORCID,Beadle Beth M.11ORCID

Affiliation:

1. The University of Texas MD Anderson Cancer Center, Houston, TX

2. Guy's and St Thomas' Hospital, London, United Kingdom

3. Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa

4. University of Alabama and Birmingham, Birmingham, AL

5. Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa

6. Benavides Cancer Institute, University of Santo Tomas, Manila, Philippines

7. Ocean Road Cancer Institute, Dar es Salaam, Tanzania

8. University of the Free State, Bloemfontein, South Africa

9. University of California San Francisco, San Francisco, CA

10. University Hospitals Plymouth, Plymouth, United Kingdom

11. Stanford University, Stanford, CA

Abstract

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.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

Cancer Research,Oncology

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