Modeling of Gamma Index for Prediction of Pretreatment Quality Assurance in Stereotactic Body Radiation Therapy of the Liver.

Purpose: The purpose of this study was to develop a predictive model to evaluate pretreatment patient-specific quality assurance (QA) based on treatment planning parameters for stereotactic body radiation therapy (SBRT) for liver carcinoma.

Materials and methods: We retrospectively selected 180 cases of liver SBRT treated using the volumetric modulated arc therapy technique. Numerous parameters defining the plan complexity were calculated from the DICOM-RP (Radiotherapy Plan) file using an in-house program developed in MATLAB. Patient-specific QA was performed with global gamma evaluation criteria of 2%/2 mm and 3%/3 mm in a relative mode using the Octavius two-dimensional detector array. Various statistical tests and multivariate predictive models were evaluated.

Results: The leaf speed (MI_LS) and planning target volume size showed the highest correlation with the gamma criteria of 2%/2 mm and 3%/3 mm (P < 0.05). Degree of modulation (DoM), MCS_SPORT, leaf speed (MI_LS), and gantry speed (MI_GS) were predictors of global gamma pass rate (GPR) for 2%/2 mm (G22), whereas DoM, MCS_SPORT, leaf speed (MI_LS) and robust decision making were predictors of the global GPR criterion of 3%/3 mm (G33). The variance inflation factor values of all predictors were <2, indicating that the data were not associated with each other. For the G22 prediction, the sensitivity and specificity of the model were 75.0% and 75.0%, respectively, whereas, for G33 prediction, the sensitivity and specificity of the model were 74.9% and 85.7%%, respectively.

Conclusions: The model was potentially beneficial as an easy alternative to pretreatment QA in predicting the uncertainty in plan deliverability at the planning stage and could help reduce resources in busy clinics.