Rolling out RapidPlan: What we've learnt. Issue 4 (3rd September 2020)

• Record Type: Journal Article
• Authors: van Gysen, Kirsten; O'Toole, James; Le, Andrew; Wu, Kenny; Schuler, Thilo; Porter, Brian; Kipritidis, John; Atyeo, John; Brown, Chris; Eade, Thomas
• Is Part Of: Journal of medical radiation sciences. Volume 67:Issue 4(2020:Dec.)
• Page Start: 310
• Page End: 317
• Publication Date: 2020-09-03
• Subjects: Knowledge‐based planning -- radiation therapy -- RapidPlan; Radiology, Medical -- Periodicals; Radiology, Medical -- Australia -- Periodicals; Radiology, Medical -- New Zealand -- Periodicals; Radiotherapy -- Periodicals; Diagnostic imaging -- Periodicals; 616
• DOI: 10.1002/jmrs.420
• Languages: English
• ISSNs: 2051-3895

Abstract

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. Abstract : Knowledge‐based planning, RapidPlan (RP), aims to consistently improve plan quality and efficiency in a radiotherapy department. We discuss our experience with the implementation, validation and outcomes associated with the use of RP in a single institution. 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.