Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care
Laurence E. Court, Ajay Aggarwal, Anuja Jhingran, Komeela Naidoo, Tucker Netherton, Adenike Olanrewaju, Christine Peterson, Jeannette Parkes, Hannah Simonds, Christoph Trauernicht, Lifei Zhang, Beth M. Beadle, Shareen Ahmad, David Anderson, Arjig Baghwala, Karen Chan, Prajnan Das, Albert Edwards, May Elbanna, Hesham Elhalawani, Medhat Elsayed, Agnes Ewongwo, Nazia Fakie, C. David Fuller, Adam Garden, Matt Gove, Teresa Guerrero-Urbano, Njeri Kaittany, Mishal Khan, Joshua Langer, Percy Leeig, Becky Lee, Anna Lee, Belinda Lee, Michelle Leech, Ben Li, Katie Lichter, Lilie Lin, Stacy Lin, Dorothy Lombe, Indranil Mallick, Sean Maroongroge, Rachael Martin, Gwendolyn McGinnis, Megan Mezera, Mustefa Mohammedsaid, Son Nguyen, Jenny Nuanjing, Tony Phillips, Surendra Prajapati, Lydia Punt, Valerie Reed, Dominique Roniger, Simona Shaitelman, Alicia Sherriff, Jay Shiao, Heath Skinner, Ashely Susan, Jordan Sutton, Hamza Syed, Sandy Thang, Juanita Thompson, Gary Walker, Julie Wetter, Ingrid White, Melody Xu, Yousif Yousif, Simeng Zhu,- Cancer Research
- Oncology
PURPOSE
Increased automation has been identified as one approach to improving global cancer care. The Radiation Planning Assistant (RPA) is a web-based tool offering automated radiotherapy (RT) contouring and planning to low-resource clinics. In this study, the RPA workflow and clinical acceptability were assessed by physicians around the world.
METHODS
The RPA output for 75 cases was reviewed by at least three physicians; 31 radiation oncologists at 16 institutions in six countries on five continents reviewed RPA contours and plans for clinical acceptability using a 5-point Likert scale.
RESULTS
For cervical cancer, RPA plans using bony landmarks were scored as usable as-is in 81% (with minor edits 93%); using soft tissue contours, plans were scored as usable as-is in 79% (with minor edits 96%). For postmastectomy breast cancer, RPA plans were scored as usable as-is in 44% (with minor edits 91%). For whole-brain treatment, RPA plans were scored as usable as-is in 67% (with minor edits 99%). For head/neck cancer, the normal tissue autocontours were acceptable as-is in 89% (with minor edits 97%). The clinical target volumes (CTVs) were acceptable as-is in 40% (with minor edits 93%). The volumetric-modulated arc therapy (VMAT) plans were acceptable as-is in 87% (with minor edits 96%). For cervical cancer, the normal tissue autocontours were acceptable as-is in 92% (with minor edits 99%). The CTVs for cervical cancer were scored as acceptable as-is in 83% (with minor edits 92%). The VMAT plans for cervical cancer were acceptable as-is in 99% (with minor edits 100%).
CONCLUSION
The RPA, a web-based tool designed to improve access to high-quality RT in low-resource settings, has high rates of clinical acceptability by practicing clinicians around the world. It has significant potential for successful implementation in low-resource clinics.