Plan Complexity Metric Based Patient Specific Quality Assurance Outcome Prediction Across Two Machines

Purpose: This study aims to develop a prediction model for patient-specific quality assurance (PSQA) outcomes for the institution using established plan complexity metrics (PCMs) from volumetric modulated arc treatment (VMAT) plans delivered on two machines with different characteristics. Materials and Methods: One hundred VMAT plans were created using Eclipse Treatment Planning Systems in a Unique machine and analyzed for various PCMs such as modulation complexity score for VMAT (MCSv), leaf travel modulation complexity score, plan normalized monitor unit (PMU), modulation index for VMAT suggested by Li and Xing (MI SPORT ), and multileaf collimator speed and acceleration. The study was repeated with 50 clinically used plans in TrueBeam machine. PSQA Gamma Pass Rates (GPRs) were recorded for criteria 3%/3 mm, 3%/2 mm, 3%/1 mm, 2%/3 mm, and 2%/2 mm. The influence of PCMs on GPRs was assessed using Pearson’s correlation, and linear regression models were developed and validated. Results: The validation results demonstrated the predictive potential of the models, with deviation in the Unique falling within 3% for GPRs 3%/3 mm and 2%/3 mm, and in the TrueBeam falling within 1% for GPRs 3%/3 mm, 3%/2 mm, and 3%/1 mm, respectively. Conclusions: The PCM-based prediction tool developed has a high potential to predict PSQA results with < 3% error for the Unique Machine and with <1% error for the TrueBeam Machine. This tool directly computes GPRs, offering a simpler and more efficient evaluation method. The tool more effectively predicts spatial accuracy than dosimetric accuracy and demonstrates its sensitivity to machine-specific characteristics.