Radiation Therapy Physics Quality Assurance and Management Practices in Low- and Middle-Income Countries: An Initial Pilot Survey in Six Countries and Validation Through a Site Visit.

Purpose: Our purpose was to assess physics quality assurance (QA) practices in less resourced radiation therapy (RT) centers to improve quality of care. Methods and Materials: A preliminary study was conducted in 2020 of 13 select RT centers in 6 countries, and in 2021, our team conducted onsite visits to all the RT centers in Ghana, one of the countries from the initial survey. The RT centers included 1 private and 2 public institutions (denoted as Public-1 and Public-2). Follow-up surveys were sent to 17 medical physicists from the site visit. Questions centered on the topics of equipment, institutional practice, physics quality assurance, management, and safety practices. Qualitative and descriptive methods were used for data analysis. Questions regarding operational challenges (machine downtime, patient-related issues, power outages, and staffing) were asked on a 5-point Likert scale. Results: The preliminary survey from 2020 had a 92% response rate. One key result showed that for RT centers in lower gross national income per capita countries there was a direct correlation between QA needs and the gross national income per capita of the country. The needs identified included film/array detectors, independent dose calculation software, calibration of ion chambers, diodes, thermoluminiscence diodes (TLDs), phantoms for verification, Treatment Planning System (TPS) test phantoms, imaging test phantoms and film dosimeters, education, and training. For the post survey after the site visit in 2021, we received a 100% response rate. The private and the Public-1 institutions each have computed tomography simulators located in their RT center. The average daily patient external beam workload for each clinic on a linear accelerator was: private = 25, Public-1 = 55, Public-2 = 40. The Co-60 workload was: Public-1 = 45, Public-2 = 25 (there was no Co-60 at the private hospital). Public-1 and -2 lacked the equipment necessary to conform to best practices in Task Group reports (TG) 142 and 198. Public-2 reported significant operational challenges. Notably, Public-1 and -2 have peer review chart rounds, which are attended by clinical oncologists, medical physicists, physicians, and physics trainees. All 17 physicists who responded to the post site visit survey indicated they had a system of documenting, tracking, and trending patient-related safety incidents, but only 1 physicist reported using International Atomic Energy Agency Safety in Radiation Oncology. Conclusions: The preliminary study showed a direct correlation between QA needs and the development index of a country, and the follow-up survey examines operational and physics QA practices in the RT clinics in Ghana, one of the initial countries surveyed. This will form the basis of a planned continent-wide survey in Africa intended to spotlight QA practices in low- and middle-income countries, the challenges faced, and lessons learned to help understand the gaps and needs to support local physics QA and management programs. Audits during the site visit show education and training remain the most important needs in operating successful QA programs.

Africa's readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the way.

BACKGROUND: There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role in radiotherapy practice. However, prior to the introduction of the technology, there are certain general recommendations that must be achieved. Also, the current challenges of AI must be addressed. In this review, we assess how Africa is prepared for the integration of AI technology into radiotherapy service delivery. METHODS: To assess the readiness of Africa for integration of AI in radiotherapy services delivery, a narrative review of the available literature from PubMed, Science Direct, Google Scholar, and Scopus was conducted in the English language using search terms such as Artificial Intelligence, Radiotherapy in Africa, Machine Learning, Deep Learning, and Quality Assurance. RESULTS: We identified a number of issues that could limit the successful integration of AI technology into radiotherapy practice. The major issues include insufficient data for training and validation of AI models, lack of educational curriculum for AI radiotherapy-related courses, no/limited AI teaching professionals, funding, and lack of AI technology and resources. Solutions identified to facilitate smooth implementation of the technology into radiotherapy practices within the region include: creating an accessible national data bank, integrating AI radiotherapy training programs into Africa's educational curriculum, investing in AI technology and resources such as electronic health records and cloud storage, and creation of legal laws and policies to support the use of the technology. These identified solutions need to be implemented on the background of creating awareness among health workers within the radiotherapy space. CONCLUSION: The challenges identified in this review are common among all the geographical regions in the African continent. Therefore, all institutions offering radiotherapy education and training programs, management of the medical centers for radiotherapy and oncology, national and regional professional bodies for medical physics, ministries of health, governments, and relevant stakeholders must take keen interest and work together to achieve this goal.

Validation of Monte Carlo-based calculations for megavolt electron beams for IORT and FLASH-IORT.

In Intra-Operative Radiation Therapy (IORT) the tumour site is surgically exposed and normal tissue located around the tumour may be avoided. Electron applicators would require large surgical incisions; therefore, the preferred mechanism for beam collimation is the IORT cone system. FLASH radiotherapy (FLASH-RT) involves the treatment of tumours at ultra-high dose rates and the IORT cone system can also be used. This study validates the Monte Carlo-based calculations for these small electron beams to accurately determine the dose characteristics of each possible cone-energy combination as well as custom-built alloy cutouts attached to the end of the IORT cone. This will contribute to accurate dose distribution and output factor calculations that are essential to all radiation therapy treatments. A Monte Carlo (MC) model was modelled for electron beams produced by a Siemens Primus LINAC and the IORT cones. The accelerator was built with the component modules available in the BEAMnrc code. The phase-space file generated by the BEAM simulation was used as the source input for the subsequent DOSXYZnrc simulations. Percentage Depth Dose (PDD) data and profiles were extracted from the dose distributions obtained with the DOSXYZnrc simulations. These beam characteristics were compared with measured data for 6, 12, and 18 MeV electron beams for the IORT open cones of diameters 19, 45, and 64 mm and irregularly shaped cutouts. The MC simulations could replicate electron beams within a criterion of 3%/3 mm. Applicator factors were within 0.7%, and cone factors showed good agreement, except for the 9 mm cone size. Based on the successful comparisons between measurement and MC-calculated dose distributions, output factors for the open cones and for small irregularly shaped IORT beams, it may be concluded that the Monte Carlo based dose calculation could replicate electron beams used for IORT and FLASH-IORT.