Implementing Remote Radiotherapy Planning to Increase Patient Flow at a Johannesburg Academic Hospital, South Africa: Protocol for a Prospective Feasibility Study

Abstract

Background: Access to timely radiotherapy in resource-constrained environments, particularly low- and middle-income countries (LMIC), is hampered by infrastructure constraints, workforce shortages, and a rising cancer burden. Remote radiotherapy planning (treatment planning as a service [TPaaS]) has the potential to enhance workflow efficiency, reduce wait times, and expand access to treatment. However, its integration and feasibility in LMIC public health systems remain underexplored.

Objective: This study evaluates the feasibility and initial effectiveness of remote radiotherapy planning using the Varian Eclipse system integrated with Elekta Versa HD linear accelerators (LINACs) at the busiest public hospital in South Africa. The primary goal is to determine whether remote planning can maintain plan quality while enhancing efficiency and minimizing treatment delays.

Methods: A prospective, single-site, pilot study is being conducted at Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) in 2 phases. Phase 1 (feasibility) encompasses system commissioning, including beam modeling, computed tomography (CT)-to-electron density calibration, multileaf collimator (MLC) optimization, and dose calculations using the anisotropic analytical algorithm. System performance is validated through gamma index analysis (≥95% pass at 3%/3 mm). Interoperability and workflow readiness are assessed using simulated clinical scenarios and time integration steps. Phase 2 (effectiveness/impact) evaluates operational outcomes in 100 screened adult patients (≥18 years) with cervical, breast, prostate, head and neck, or rectal cancers requiring curative radiotherapy. Patients are grouped by cancer type (25 per group). Time to treatment, plan quality, and system efficiency will be compared with historical in-person planning data. Key workflow metrics include dates of first consultation, CT simulation, planning initiation, plan approval, quality assurance, and treatment start and completion.

Results: The study commenced enrollment in November 2023, with completion anticipated by mid-2025. As of July 2024, approximately 44 patients were screened and are anticipated to complete the remote planning. Initial findings show successful MLC transmission and dosimetric leaf gap optimization through iterative testing. Gamma pass rates exceeded 90% on both clinical and test servers, demonstrating initial accuracy. Results, including planning timelines, quality assurance outcomes, and system performance, will be available following comprehensive analysis in the third quarter of 2025. Preliminary findings indicate effective integration of remote planning in a resource-constrained public health sector setting.

Conclusions: This study shows that remote radiotherapy planning is feasible and might improve cancer treatment in LMIC. The integration of commercially available systems, such as TPaaS, was successfully achieved without compromising dosimetric quality and ensured workflow continuity. Remote planning could serve as an effective tool to reduce treatment delays and enhance resource utilization in oncology units facing high demand. These findings offer valuable insights into technical integration, quality planning, and workflow outcomes, which can inform the future implementation of effective strategies in resource-constrained settings.

International registered report identifier (irrid): DERR1-10.2196/60131.

Keywords: cancer care; feasibility; quality assurance; radiotherapy; remote radiotherapy; remote radiotherapy planning services; remote treatment; treatment planning.

Figure 1

CONSORT (Consolidated Standards of Reporting Trials) flowchart of the remote radiotherapy treatment planning service.

Figure 2

Critical decision points and corresponding time stamps are meticulously recorded at each stage of the process. QA: quality assurance; RO: radiation oncologist.

Figure 3

Remote radiotherapy planning steps. AOS: advanced oncology solutions; AIRC: artificial intelligence companion radiation-based radiotherapy; CT: computed tomography; CTV: clinical target volume; DR: dose rate; LINAC: linear accelerator; Msq: Mosaiq; MU: monitor unit; PTV: planning target volume; QA: quality assurance; TPS: treatment planning system; ToI: tolerance; Tx: radiation treatment or treatment plan; RTT: radiation therapist; VMAT: volumetric modular arc therapy.