TY - JOUR
T1 - Addressing challenges in low-income and middle-income countries through novel radiotherapy research opportunities
AU - Abdel-Wahab, May
AU - Coleman, C. Norman
AU - Eriksen, Jesper Grau
AU - Lee, Peter
AU - Kraus, Ryan
AU - Harsdorf, Ekaterina
AU - Lee, Becky
AU - Dicker, Adam
AU - Hahn, Ezra
AU - Agarwal, Jai Prakash
AU - Prasanna, Pataje G.S.
AU - MacManus, Michael
AU - Keall, Paul
AU - Mayr, Nina A.
AU - Jereczek-Fossa, Barbara Alicja
AU - Giammarile, Francesco
AU - Kim, In Ah
AU - Aggarwal, Ajay
AU - Lewison, Grant
AU - Lu, Jiade J.
AU - Guedes de Castro, Douglas
AU - Kong, Feng Ming (Spring)
AU - Afifi, Haidy
AU - Sharp, Hamish
AU - Vanderpuye, Verna
AU - Olasinde, Tajudeen
AU - Atrash, Fadi
AU - Goethals, Luc
AU - Corn, Benjamin W.
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/6
Y1 - 2024/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85194037565&partnerID=8YFLogxK
U2 - 10.1016/S1470-2045(24)00038-X
DO - 10.1016/S1470-2045(24)00038-X
M3 - Review
C2 - 38821101
AN - SCOPUS:85194037565
SN - 1470-2045
VL - 25
SP - e270-e280
JO - The Lancet Oncology
JF - The Lancet Oncology
IS - 6
ER -