Abstract
PurposeIntensity-modulated proton therapy (IMPT) treatments are increasing, however, treatment planning remains complex and prone to variability. RapidPlanTMPT (Varian Medical Systems, Palo Alto, California, USA) is a pre-clinical, proton-specific, automated knowledge-based planning solution which could reduce variability and increase efficiency. It uses a library of previous IMPT treatment plans to generate a model which can predict organ-at-risk (OAR) dose for new patients, and guide IMPT optimization. This study details and evaluates RapidPlanTMPT.MethodsIMPT treatment plans for 50 head-and-neck cancer patients populated the model-library. The model was then used to create knowledge-based plans (KBPs) for 10 evaluation-patients. Model quality and accuracy were evaluated using model-provided OAR regression plots and examining the difference between predicted and achieved KBP mean dose. KBP quality was assessed through comparison with respective manual IMPT plans on the basis of boost/elective planning target volume (PTVB/PTVE) homogeneity and OAR sparing. The time to create KBPs was recorded.ResultsModel quality was good, with an average R2 of 0.85 between dosimetric and geometric features. The model showed high predictive accuracy with differences of <3 Gy between predicted and achieved OAR mean doses for 88/109 OARs. On average, KBPs were comparable to manual IMPT plans with differences of <0.6% in homogeneity. Only 2 of 109 OARs in KBPs had a mean dose >3 Gy more than the manual plan. On average, dose-volume histogram (DVH) predictions required 0.7 minutes while KBP optimization and dose calculation required 4.1 minutes (a ‘continue optimization’ phase, if required, took an additional 2.8 minutes, on average).ConclusionsRapidPlanTMPT demonstrated efficiency and consistency and IMPT KBPs were comparable to manual plans. Because worse OAR sparing in a KBP was not always associated with geometric-outlier warnings, manual plan checks remain important. Such an automated planning solution could also assist in clinical trial quality assurance and overcome the learning curve associated with IMPT.
Highlights
The growing interest in proton therapy is substantiated by the recent increase in the number of treatment centers globally
knowledge-based plans (KBPs) were comparable to manual Intensity-modulated proton therapy (IMPT) plans with differences of
RapidPlanTMPT demonstrated efficiency and consistency and IMPT KBPs were comparable to manual plans
Summary
The growing interest in proton therapy is substantiated by the recent increase in the number of treatment centers globally. It is well documented that even established photon techniques like intensitymodulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT) suffer from a wide variation in treatment plan quality between planners and institutes [1,2]. It is, reasonable to assume that this will be a problem for newer, increasingly complex modalities such as intensitymodulated proton therapy (IMPT). One example of a solution to address the variation observed in photon treatment planning, and to try and improve planning efficiency, is RapidPlanTM (Varian Medical Systems, Palo Alto, California, USA), a knowledge-based automated planning
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