Rolling out RapidPlan: What we've learnt

Abstract

Introduction: RapidPlan (RP), a knowledge-based planning system, aims to consistently improve plan quality and efficiency in radiotherapy. During the early stages of implementation, some of the challenges include knowing how to optimally train a model and how to integrate RP into a department. We discuss our experience with the implementation of RP into our institution. Methods: We reviewed all patients planned using RP over a 7-month period following inception in our department. Our primary outcome was clinically acceptable plans (used for treatment) with secondary outcomes including model performance and a comparison of efficiency and plan quality between RP and manual planning (MP). Results: Between November 2017 and May 2018, 496 patients were simulated, of which 217 (43.8%) had an available model. RP successfully created a clinically acceptable plan in 87.2% of eligible patients. The individual success of the 24 models ranged from 50% to 100%, with more than 90% success in 15 (62.5%) of the models. In 40% of plans, success was achieved on the 1st optimisation. The overall planning time with RP was reduced by up to 95% compared with MP times. The quality of the RP plans was at least equivalent to historical MP plans in terms of target coverage and organ at risk constraints. Conclusion: While initially time-consuming and resource-intensive to implement, plans optimised with RP demonstrate clinically acceptable plan quality, while significantly improving the efficiency of a department, suggesting RP and its application is a highly effective tool in clinical practice.

Artificial intelligence technology for radiation oncology understaff mitigation and cost-effective treatment planning

“…37 To properly evaluate the efficacy of RapidPlan as a tool for multicenter clinical study design and quality assessment, RapidPlan validation should be undertaken in a planned clinical trial dataset for which a meaningful planning comparison can be done. 19 By evaluation of the published data, it could be concluded that RapidPlan can largely benefit radiation therapy institutions by streamlining the treatment planning process. In some cases, it made possible a time reduction of over 90%, which become even more impactful when dealing with challenging cases like HS-WBRT planning, where the manual treatment planning takes around 9 hours to be completed.…”

“…Source: 19 In addition, automated planning has been used for challenging cases of hippocampal-sparing whole brain irradiation [HS-WBRT] in a study conducted in Chicago. 24 For HS-WBRT, planning and delivery were totally automated thanks to HyperArc [HA] 25 technology and a RP model.…”

“…A comprehensive study from an Australian institution describes their experience after implementing RapidPlan for 7 months and creating plans for 496 patients for a variety of anatomical regions. 19 They concluded that plans optimized using RapidPlan show clinically acceptable quality while greatly increasing the workflow efficiency. RapidPlan not only produced plans of at least equivalent quality to those created manually, but also shortened planning time by more than 80% in the majority of subsites.…”

Hippocampal avoidance whole brain radiotherapy: developing a RapidPlan model

“…To alleviate these challenges, we explore the use of a knowledge-based automated optimisation engine, RapidPlan (Varian Medical Systems, Palo Alto, CA, USA), which is an addition into the Eclipse treatment planning system and aims to achieve improved plan quality and consistency leading to better efficiency in planning (5). The use of RapidPlan has been widely assessed for different anatomical sites (6)(7)(8)(9)(10)(11)(12)(13) with this article outlining the creation and testing of an automated HA-WBRT RapidPlan model (Auto).…”