Barriers and Facilitators of Implementing Automated Radiotherapy Planning: A Multisite Survey of Low- and Middle-Income Country Radiation Oncology Providers.

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.

Bridging the Radiotherapy Education Gap in Africa: Lessons Learnt from the Cape Town Access to Care Training Programme Over the Past 5 Years (2015-2019).

The role of radiotherapy (RT) in cancer care is well described, with a clear correlation between access to radiotherapy and overall survival. Cancer mortality rates in Africa are substantially higher than those of the rest of the world, which may be partly attributed to lack of RT access and insufficient human resources. The Access to Care (A2C) Cape Town RT training programme was created in 2014 with the aim of supplementing practical RT training in the region, focusing on clinics moving from 2 to 3D conformal radiotherapy (3DCRT). The programme makes use of hybrid teaching methods, including pre-course e-learning followed by 17 on-site days of free-thinking design exercises, didactic learning, hands-on treatment planning computer sessions (39% of total teaching time), virtual simulation training and departmental demonstration sessions. Email support is offered to all teams for 3 months after each course to develop clinical protocols. Thirteen teams (radiation oncologist, medical physicist and radiation therapy technologist) from Africa attended the course between 2015 and 2019, with additional participants from seven South African and four international centres. E-learning done on the LäraNära training platform was only successful once formal progress tracking was introduced in 2019 (34% vs. 76% test completion rate). Delays between course attendance and initial clinical use of equipment proved to be detrimental to knowledge retention, with some centres having to send a second team for training. The course will be modified for remote teaching in 2021, to make provision for the global changes in travel due to Covid-19.

A novel external beam radiotherapy method for cervical cancer patients using virtual straight or bending boost areas; an in-silico feasibility study.

AIM: To investigate the potential role of a novel spatially fractionated radiation therapy (SFRT) method where heterogeneous dose patterns are created in target areas with virtual rods, straight or curving, of variable position, diameter, separation and alignment personalised to a patient's anatomy. The images chosen for this study were CT scans acquired for the external beam part of radiotherapy. METHODS: Ten patients with locally advanced cervical cancer were retrospectively investigated with SFRT. The dose prescription was 30 Gy in 5 fractions to 90% target volume coverage. Peak-and-valley (SFRT_1) and peak-only (SFRT_2) strategies were applied to generate the heterogeneous dose distributions. The planning objectives for the target (CTV) were D90% ≥ 30 Gy, V45Gy ≥ 50-55% and V60Gy ≥ 30%. The planning objectives for the organs at risk (OAR) were: D2cm3 ≤ 23.75 Gy, 17.0 Gy, 19.5 Gy, 17.0 Gy for the bladder, rectum, sigmoid and bowel, respectively. The plan comparison was performed employing the quantitative analysis of the dose-volume histograms. RESULTS: The D2cm3 was 22.4 ± 2.0 (22.6 ± 2.1) and 13.9 ± 2.9 (13.2 ± 3.0) for the bladder and the rectum for SFRT_1 (SFRT_2). The results for the sigmoid and the bowel were 2.6 ± 3.1 (2.8 ± 3.0) and 9.1 ± 5.9 (9.7 ± 7.3), respectively. The hotspots in the target volume were V45Gy = 43.1 ± 7.5% (56.6 ± 5.6%) and V60Gy = 15.4 ± 5.6% (26.8 ± 6.6%) for SFRT_1 (SFRT_2). To account for potential uncertainties in the positioning, the dose prescription could be escalated to D90% = 33-35 Gy to the CTV without compromising any constraints to the OARs CONCLUSION: In this dosimetric study, the proposed novel planning technique for boosting the cervix uteri was associated with high-quality plans, respecting constraints for the organs at risk and approaching the level of dose heterogeneity achieved with routine brachytherapy. Based on a sample of 10 patients, the results are promising and might lead to a phase I clinical trial.

Automated treatment planning of postmastectomy radiotherapy.

PURPOSE: Breast cancer is the most common cancer in women globally and radiation therapy is a cornerstone of its treatment. However, there is an enormous shortage of radiotherapy staff, especially in low- and middle-income countries. This shortage could be ameliorated through increased automation in the radiation treatment planning process, which may reduce the workload on radiotherapy staff and improve efficiency in preparing radiotherapy treatments for patients. To this end, we sought to create an automated treatment planning tool for postmastectomy radiotherapy (PMRT). METHODS: Algorithms to automate every step of PMRT planning were developed and integrated into a commercial treatment planning system. The only required inputs for automated PMRT planning are a planning computed tomography scan, a plan directive, and selection of the inferior border of the tangential fields. With no other human input, the planning tool automatically creates a treatment plan and presents it for review. The major automated steps are (a) segmentation of relevant structures (targets, normal tissues, and other planning structures), (b) setup of the beams (tangential fields matched with a supraclavicular field), and (c) optimization of the dose distribution by using a mix of high- and low-energy photon beams and field-in-field modulation for the tangential fields. This automated PMRT planning tool was tested with ten computed tomography scans of patients with breast cancer who had received irradiation of the left chest wall. These plans were assessed quantitatively using their dose distributions and were reviewed by two physicians who rated them on a three-tiered scale: use as is, minor changes, or major changes. The accuracy of the automated segmentation of the heart and ipsilateral lung was also assessed. Finally, a plan quality verification tool was tested to alert the user to any possible deviations in the quality of the automatically created treatment plans. RESULTS: The automatically created PMRT plans met the acceptable dose objectives, including target coverage, maximum plan dose, and dose to organs at risk, for all but one patient for whom the heart objectives were exceeded. Physicians accepted 50% of the treatment plans as is and required only minor changes for the remaining 50%, which included the one patient whose plan had a high heart dose. Furthermore, the automatically segmented contours of the heart and ipsilateral lung agreed well with manually edited contours. Finally, the automated plan quality verification tool detected 92% of the changes requested by physicians in this review. CONCLUSIONS: We developed a new tool for automatically planning PMRT for breast cancer, including irradiation of the chest wall and ipsilateral lymph nodes (supraclavicular and level III axillary). In this initial testing, we found that the plans created by this tool are clinically viable, and the tool can alert the user to possible deviations in plan quality. The next step is to subject this tool to prospective testing, in which automatically planned treatments will be compared with manually planned treatments.

A risk assessment of automated treatment planning and recommendations for clinical deployment.

PURPOSE: To assess the risk of failure of a recently developed automated treatment planning tool, the radiation planning assistant (RPA), and to determine the reduction in these risks with implementation of a quality assurance (QA) program specifically designed for the RPA. METHODS: We used failure mode and effects analysis (FMEA) to assess the risk of the RPA. The steps involved in the workflow of planning a four-field box treatment of cervical cancer with the RPA were identified. Then, the potential failure modes at each step and their causes were identified and scored according to their likelihood of occurrence, severity, and likelihood of going undetected. Additionally, the impact of the components of the QA program on the detectability of the failure modes was assessed. The QA program was designed to supplement a clinic's standard QA processes and consisted of three components: (a) automatic, independent verification of the results of automated planning; (b) automatic comparison of treatment parameters to expected values; and (c) guided manual checks of the treatment plan. A risk priority number (RPN) was calculated for each potential failure mode with and without use of the QA program. RESULTS: In the RPA automated treatment planning workflow, we identified 68 potential failure modes with 113 causes. The average RPN was 91 without the QA program and 68 with the QA program (maximum RPNs were 504 and 315, respectively). The reduction in RPN was due to an improvement in the likelihood of detecting failures, resulting in lower detectability scores. The top-ranked failure modes included incorrect identification of the marked isocenter, inappropriate beam aperture definition, incorrect entry of the prescription into the RPA plan directive, and lack of a comprehensive plan review by the physician. CONCLUSIONS: Using FMEA, we assessed the risks in the clinical deployment of an automated treatment planning workflow and showed that a specialized QA program for the RPA, which included automatic QA techniques, improved the detectability of failures, reducing this risk. However, some residual risks persisted, which were similar to those found in manual treatment planning, and human error remained a major cause of potential failures. Through the risk analysis process, we identified three key aspects of safe deployment of automated planning: (a) user training on potential failure modes; (b) comprehensive manual plan review by physicians and physicists; and (c) automated QA of the treatment plan.