Radiotherapy Innovation for Low-Income Settings: AI, Deep Phenotyping, and Real-Time Targeting
This Lancet Oncology Series paper examines the state of radiotherapy research in low- and middle-income countries, presenting new IAEA survey data alongside an analysis of emerging technologies -- including AI, deep phenotyping, and real-time tumor targeting -- with applicability to resource-constrained settings. The authors argue that innovations originally developed for high-resource environments can be adapted to address the pressing cancer burden in LMICs, and highlight best practices for building the research workforce of the future.
The original study
Addressing challenges in low-income and middle-income countries through novel radiotherapy research opportunities.
- Authors
- Abdel-Wahab M, Coleman CN, Eriksen JG, Lee P, Kraus R, Harsdorf E, et al.
- Journal
- The Lancet. Oncology
- Type
- Journal Article, Review
- PMID
- 38821101
Original abstract
Although radiotherapy continues to evolve as a mainstay of the oncological armamentarium, research and innovation in radiotherapy in low-income and middle-income countries (LMICs) faces challenges. This third Series paper examines the current state of LMIC radiotherapy research and provides new data from a 2022 survey undertaken by the International Atomic Energy Agency and new data on funding. In the context of LMIC-related challenges and impediments, we explore several developments and advances-such as deep phenotyping, real-time targeting, and artificial intelligence-to flag specific opportunities with applicability and relevance for resource-constrained settings. Given the pressing nature of cancer in LMICs, we also highlight some best practices and address the broader need to develop the research workforce of the future. This Series paper thereby serves as a resource for radiation professionals.