Author: Shane McCarthy, Damodar Pokhrel, William St. Clair, Eddy S Yang 👨🔬
Affiliation: University of Kentucky, Department of Radiation Medicine 🌍
Purpose: Demonstration of automated stereotactic treatment planning for recurrent head and neck (RHN) cancers using knowledge-based planning, multicriteria optimization (MCO), and dosimetric scorecards while maintaining an effective target dose with improved organs-at-risk (OARs) sparing.
Methods: Four previously treated RHN HyperArc patients, prescribed 30-40 Gy in five fractions, were gathered. For each patient a unique dosimetric scorecard was created and beam geometry was established using HyperArc. An Eclipse Scripting script then automatically generated a plan using an in-house RapidPlan model. Next, the plan was fed to a custom tradeoff exploration algorithm which systematically explored the MCO’s Pareto frontier and evaluated the Pareto plan’s score from the scorecard. A deliverable plan was generated from the best scoring tradeoff costs. Final dose, calculated with AcurosXB, was normalized to the original plan’s PTV D95%. Maximum doses to OARs, target metrics, and calculation/optimization times were recorded.
Results: The automated treatment process took on average 12.1 ± 0.9 minutes to generate the RapidPlan plan and an additional 25.0 ± 2.6 minutes for MCO exploration. The GTV mean dose stayed comparable across all plans with an average decrease of 0.8 ± 0.9 Gy from the original plan to the RapidPlan and an average increase of 0.3 ± 1.2 Gy from the original plan to the MCO plan. The PTV Paddick’s conformity index received an average decrease of 0.01 ± 0.01 from original to RapidPlan and an increase of 0.02 ± 0.01 from original to MCO. All OAR maximum dose changes were favorable or remained within clinically acceptable dose levels for stereotactic delivery.
Conclusion: The process established throughout this work offers a nearly fully automated treatment planning routine demonstrated for stereotactic RHN patients. Through automation, the treatment planning workflow is standardized and quickened, improving the clinical workflow and allowing shortened simulation to treatment times, potentially improving clinical outcomes.