Carlos Cardenas
Assistant Professor
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Articles (14)
Year:
2024
Year:
2024
Year:
2024
Year:
2023
Year:
2023
Year:
2022
Year:
2022
Year:
2022
Year:
2022
PURPOSE Global access to radiotherapy (RT) is inequitable, with obstacles to implementing modern technologies in low- and middle- income countries (LMICs). The Radiation Planning Assistant (RPA) is a web-based automated RT planning software package intended to increase accessibility of high-quality RT planning. We surveyed LMIC RT providers to identify barriers and facilitators of future RPA deployment and uptake. METHODS RT providers underwent a pilot RPA teaching session in sub-Saharan Africa (Botswana, South Africa, and Tanzania) and Central America (Guatemala). Thirty providers (30 of 33, 90.9% response rate) participated in a postsession survey. RESULTS Respondents included physicians (n = 10, 33%), physicists (n = 9, 30%), dosimetrists (n = 8, 27%), residents/registrars (n = 1, 3.3%), radiation therapists (n = 1, 3.3%), and administrators (n = 1, 3.3%). Overall, 86.7% expressed interest in RPA; more respondents expected that RPA would be usable in 2 years (80%) compared with now (60%). Anticipated barriers were lack of reliable internet (80%), potential subscription fees (60%), and need for functionality in additional disease sites (48%). Expected facilitators included decreased workload (80%), decreased planning time (72%), and ability to treat more patients (64%). Forty-four percent anticipated that RPA would help transition from 2-dimensional to 3-dimensional techniques and 48% from 3-dimensional to intensity-modulated radiation treatment. Of a maximum acceptability/feasibility score of 60, physicians (45.6, standard deviation [SD] = 7.5) and dosimetrists (44.3, SD = 9.1) had lower scores than the mean for all respondents (48.3, SD = 7.7) although variation in scores by roles was not significantly different ( P = .21). CONCLUSION These data provide an early assessment and create an initial framework to identify stakeholder needs and establish priorities to address barriers and promote facilitators of RPA deployment and uptake across global sites, as well as to tailor to needs in LMICs.
Year:
2022
Year:
2021
Year:
2021
Year:
2021
Year:
2021
Collaborators (13)
Skylar Gay
The University of Texas MD Anderson Cancer Center
Laurence Court
Associate Professor
The University of Texas MD Anderson Cancer Center
Jack Phan
Physician/Associate Professor
MD Anderson Cancer Center
Zohaib Iqbal
UAB Hospital
Kunio Nakamura
Assistant Professor
Cleveland Clinic
Beth M. Beadle
Stanford University
Matthew Ning
The University of Texas MD Anderson Cancer Center
Gerd Heilemann
Medical University of Vienna
David Jaffray
Professor
The University of Texas MD Anderson Cancer Center
Thomas Purdie
Associate Professor
University of Toronto
Grace L. Smith
The University of Texas MD Anderson Cancer Center
Abdallah Mohamed
Assistant Professor
Baylor College of Medicine
Rex Cardan
The University of Alabama
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