International Journal of Radiation Oncology*Biology*Physics
Physics ContributionEvaluation of a Knowledge-Based Planning Solution for Head and Neck Cancer
Introduction
Variations in knowledge and experience can lead to large differences in the quality of radiation therapy treatment plans 1, 2 and may compromise the gains that can be realized with advanced technologies such as intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). The same holds true for labor and computing resources, which can affect the implementation of new treatment planning techniques and treatment planning capacity. Various solutions are being investigated to improve planning consistency 3, 4, 5, 6, 7, including increased automation of planning by using knowledge-based approaches 8, 9, 10, 11, 12, 13. These approaches typically use libraries of existing patient plans to create models that predict the amount of organ-at-risk (OAR) sparing that can be achieved for a new patient, based, for example, on planning target volume (PTV)-OAR distance and overlap (14). Being able to rationally predict OAR dose-volume histograms (DVHs) could remove the need for performing interactive optimization or multiple iterative optimizations and could increase consistency in treatment planning 14, 15, 16, 17, 18. Resulting plans produced by such a knowledge-based system should reflect the quality of the plans that populate the model and the ability of the software to predict the achievable DVHs.
RapidPlan (Varian Medical Systems, Palo Alto, CA) is a commercially available knowledge-based planning solution derived from previously published work 14, 16. Knowledge in this case is represented by models created from libraries of previous plans. The purpose of this report was (1) to benchmark RapidPlan performance compared to recent clinical VMAT (RapidArc, Varian) plans by using model libraries made up of different plans and with different total numbers of plans; and (2) to investigate whether model libraries based on plans that spare many OARs can be usefully applied to patients treated shortly after our clinical introduction of RapidArc, in whom fewer OARs were spared. This allowed us to test the versatility of the model libraries and to illustrate what a center new to RapidArc planning and starting with the inclusion of only a few OARs may anticipate when applying a model consisting of more advanced plans.
Section snippets
Clinical plans
Clinical locally advanced head and neck cancer (HNC) treatment plans with a simultaneous integrated boost (SIB) were created using 6-MV photons and 2 full arcs. Plans aimed to deliver 95% of the prescribed dose of 54.25 to 58.15 Gy to 98% of the elective PTV (PTVE; V95 ≥ 98%) and 95% of the prescribed dose of 70 Gy to 99% of the boost PTV (PTVB), in 35 fractions, while limiting the volume of each PTV receiving >107% (V107) of the prescribed dose. A 5-mm transition zone (PTVT) was created
Results
In EG1, an average of 1.8, 1.1, and 0.5 OARs per patient were flagged as outliers by Model30A, Model30B, and Model60, respectively, from an average of 8.3 OARs per patient. In EG2, there were 0.4, 0.5, and 0.3 outlier OARs, respectively, per patient of 2.7 OARs per patient. Model60 resulted in the least number of outliers, indicating that this model can account for a larger range of patient geometries. Although we analyzed the outlier warnings given when applying the different models,
Discussion
In this study, we evaluated the performance of a commercial knowledge-based planning solution. HNC patients were chosen for the analysis because their plans include multiple PTVs and many individual salivary and swallowing OARs, testing RapidPlan performance in a relatively challenging, although common, clinical scenario. Pooled results showed that generally, if most of the OARs in a patient were not flagged as outliers, RapidPlan provided comparable and often improved plan quality compared to
Conclusions
In conclusion, RapidPlan knowledge-based treatment planning can deliver results that are at least comparable to CP results when the patient is similar to the bulk of patients used to populate the model. Caution should be used when applying RapidPlan models to patients whose geometry falls outside the range of the constituent plans in the model, in line with the advice contained in the instruction manual 22, 23. Determining whether an individual plan produced by RapidPlan is suboptimal, defining
Acknowledgments
We thank Maria Cordero Marcos for help with analyzing the created models.
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This research was supported by a grant from Varian Medical Systems.
Conflict of interest: The department has a research collaboration with Varian Medical Systems. Drs Dahele, Slotman, and Verbakel have received honoraria and travel support from Varian Medical Systems. Drs Tol and Verbakel are members of the Varian RapidPlan council.