Physics Contribution
Evaluation of a Knowledge-Based Planning Solution for Head and Neck Cancer

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Purpose

Automated and knowledge-based planning techniques aim to reduce variations in plan quality. RapidPlan uses a library consisting of different patient plans to make a model that can predict achievable dose-volume histograms (DVHs) for new patients and uses those models for setting optimization objectives. We benchmarked RapidPlan versus clinical plans for 2 patient groups, using 3 different libraries.

Methods and Materials

Volumetric modulated arc therapy plans of 60 recent head and neck cancer patients that included sparing of the salivary glands, swallowing muscles, and oral cavity were evenly divided between 2 models, Model30A and Model30B, and were combined in a third model, Model60. Knowledge-based plans were created for 2 evaluation groups: evaluation group 1 (EG1), consisting of 15 recent patients, and evaluation group 2 (EG2), consisting of 15 older patients in whom only the salivary glands were spared. RapidPlan results were compared with clinical plans (CP) for boost and/or elective planning target volume homogeneity index, using HIB/HIE = 100 × (D2% − D98%)/D50%, and mean dose to composite salivary glands, swallowing muscles, and oral cavity (Dsal, Dswal, and Doc, respectively).

Results

For EG1, RapidPlan improved HIB and HIE values compared with CP by 1.0% to 1.3% and 1.0% to 0.6%, respectively. Comparable Dsal and Dswal values were seen in Model30A, Model30B, and Model60, decreasing by an average of 0.1, 1.0, and 0.8 Gy and 4.8, 3.7, and 4.4 Gy, respectively. However, differences were noted between individual organs at risk (OARs), with Model30B increasing Doc by 0.1, 3.2, and 2.8 Gy compared with CP, Model30A, and Model60. Plan quality was less consistent when the patient was flagged as an outlier. For EG2, RapidPlan decreased Dsal by 4.1 to 4.9 Gy on average, whereas HIB and HIE decreased by 1.1% to 1.5% and 2.3% to 1.9%, respectively.

Conclusions

RapidPlan knowledge-based treatment plans were comparable to CP if the patient's OAR-planning target volume geometry was within the range of those included in the models. EG2 results showed that a model including swallowing-muscle and oral-cavity sparing can be applied to patients with only salivary gland sparing. This may allow model library sharing between institutes. Optimal detection of inadequate plans and population of model libraries requires further investigation.

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.

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