Linking dose delivery accuracy and planning target margin in radiosurgery based on dose-volume histograms derived from measurement-guided dose reconstruction

Abstract

Abstract In radiosurgery (SRS), the geometric uncertainties of machine-related delivery including image-guidance and hence the planning target volume (PTV) are often evaluated by the end-to-end gamma (γ) comparison that carries no information about the clinical relevance of deviations of individual SRS plans during delivery quality assurance (DQA). A proof-of-concept method was proposed to derive the PTV against both the plan- and the machine-specific delivery errors directly from the clinically relevant dose-volume histograms (DVHs) using measured-guided dose reconstruction (MGDR) during DQA. A liquid-filled detector array and a rotating phantom were used to measure sixteen arc-based radiosurgery treatments with 1 and 2 mm gross tumor volume (GTV)-to-PTV margins, producing MGDR-3D dose distribution on both the phantom and the patient CT for γ index and clinical DVH evaluations, respectively. The PTV was considered optimal when the MGDR showed the desired prescription dose coverage (V pres ) of the GTV (100% in this study). Associations of the binary V pres outcomes (<or  =100%) of the GTV with the acceptance level of percent γ pass rate (γPR%) at 90 versus 95% were assessed. Further receiver operator characteristic (ROC) analysis was performed to assess the distance-to-agreement (DTA) and local dose difference (ΔD) criteria that may be suitable for treatment acceptance. From the MGDR, 100% GTV V pres was achieved in 68.8% and 100% of plans with 1 and 2 mm PTV, respectively. V pres outcomes were neither associated with γPR% at 1–2 mm DTA and 1%–3% ΔD nor the acceptance level for MGDR in the patient CT. ROC analysis shows statistically significant AUC values from 0.78–0.84 and 0.79–0.80 for MGDR phantom and patient doses, respectively. DQA by MGDR-DVH objectives offers the unique opportunity of direct assessment of the dose delivery accuracy and hence the optimal PTV without subject to the statistical correlation between γPR% and clinical metrics. Based on multi-criteria DVH objectives, clinical decision can be instantly made to adjust the treatment plan prescription.