Affiliation:
1. Varian Medical Systems Inc. Advanced Oncology Solutions Hixson Tennessee USA
2. Thompson Cancer Survival Center Cumberland Medical Center Crossville Tennessee USA
3. Department of Radiation Medicine University of Kentucky Chandler Medical Center Lexington Kentucky USA
Abstract
AbstractIntroductionHyperArc (HA) auto‐planning offers simplicity for the end user and consistently high‐quality SRS plans. The “Ask For It” (AFI) optimization strategy offers a manual planning technique that, when coupled with R50%Analytic, can be guided to deliver a plan with an intermediate dose spill “as low as reasonably achievable” and high target dose conformity. A direct comparison of SRS plan quality obtained using the manual planning AFI strategy and HA has been performed.MethodsUsing a CT data set available from the Radiosurgery Society, 54 PTVs were created and used to generate 19 individual SRS/SRT cases. Case complexity ranged from single PTV plans to multiple PTV plans with a single isocenter. PTV locations ranged from relative isolation from critical structures to lesions within 1.5 mm of the optic apparatus and abutting the brainstem. All cases were planned using both the AFI and HA optimization strategies as implemented in the Varian Medical Systems Eclipse Treatment Planning System. A range of treatment plan quality metrics were obtained including Intermediate Dose Spill (R50%), Conformity Indices CIRTOG and CIPaddick, PTV Dose Coverage (Dn%), PTV Mean Dose, and Modulation Factor. The Wilcoxon Signed Rank Sum non‐parametric statistical method was utilized to compare the obtained plan quality metrics.ResultsStatistically significant improvements were found for the AFI strategy for metrics R50%, CIRTOG, CIPaddick, and PTV Mean Dose (p < 0.001). HA achieved superior coverage for Dn% (p = 0.018), while the Modulation Factors were not significantly different for AFI compared to HA optimization (p = 0.13).ConclusionThis study provides evidence that the AFI manual planning strategy can produce high‐quality planning metrics similar to the HA auto‐planning method.