DISCUSSION
MIs
REGRESSION
This study has investigated the impact of the combination of plan complexity features on GPR using four treatment sites with variable levels of complexity and on two linacs with different MLC designs. Additionally, we included two VMAT‐specific features GS and DR variations as independent variables to build our model. Generally, MIs are known as important indicators to predict GPR reported by different studies.Two supervised learning methods were implemented (SVR using linear kernel and RFR) to analyze the data and predict the passing rates. The predicted GPR by the RFR model and SVR model versus measured GPR was plotted (Figure Both RFR and SVR (linear) were in good agreement, and none of their prediction values was greater than 100% (Figure Our study also demonstrates that the RFR model can predict GPR within a 3% error, which is in good agreement with Bozhikov et al.To the best of our knowledge, this study is the first to use SVR with linear regression to predict passing rate values; therefore, there is not much information regarding the use of this model. Nevertheless, many studies utilized SVR with the linear kernel for response prediction in another area. In 2016, Goli et al.As was demonstrated in Figures Additionally, we noted that LT/AL has minimal effect on predicting passing rate which is also shown that it has weaker correlation with GPR. Although strong and moderate correlations were observed between GPR with GS and DR, respectively, these two metrics have low impact on the accuracy of predicting passing rate (Figure We acknowledge that there were certain limitations in the current study because the model was created based on our clinical data, a limited number of cases, and using one TPS. There are always some levels of differences between different TPSs and measurement devices, which can affect the output. Therefore, multi‐institutional studies need to be conducted to include all possible differences.