An updating approach for knowledge‐based planning models to improve plan quality and variability in volumetric‐modulated arc therapy for prostate cancer

PURPOSE: The purpose of this study was to compare the dose–volume parameters and regression scatter plots of the iteratively improved RapidPlan (RP) models, specific knowledge‐based planning (KBP) models, in volumetric‐modulated arc therapy (VMAT) for prostate cancer over three periods. METHODS: A RP1 model was created from 47 clinical intensity‐modulated radiation therapy (IMRT)/VMAT plans. A RP2 model was created to exceed dosimetric goals which set as the mean values +1SD of the dose–volume parameters of RP1 (50 consecutive new clinical VMAT plans). A RP3 model was created with more strict dose constraints for organs at risks (OARs) than RP1 and RP2 models (50 consecutive anew clinical VMAT plans). Each RP model was validated against 30 validation plans (RP1, RP2, and RP3) that were not used for model configuration, and the dose–volume parameters were compared. The Cook's distances of regression scatterplots of each model were also evaluated. RESULTS: Significant differences (p < 0.05) between RP1 and RP2 were found in D(mean) (101.5% vs. 101.9%), homogeneity index (3.90 vs. 4.44), 95% isodose conformity index (1.22 vs. 1.20) for the target, V(40Gy) (47.3% vs. 45.7%), V(60Gy) (27.9% vs. 27.1%), V(70Gy) (16.4% vs. 15.2%), and V(78Gy) (0.4% vs. 0.2%) for the rectal wall, and V(40Gy) (43.8% vs. 41.8%) and V(70Gy) (21.3% vs. 20.5%) for the bladder wall, whereas only V(70Gy) (15.2% vs. 15.8%) of the rectal wall differed significantly between RP2 and RP3. The proportions of cases with a Cook's distance of <1.0 (RP1, RP2, and RP3 models) were 55%, 78%, and 84% for the rectal wall, and 77%, 68%, and 76% for the bladder wall, respectively. CONCLUSIONS: The iteratively improved RP models, reflecting the clear dosimetric goals based on the RP feedback (dose–volume parameters) and more strict dose constraints for the OARs, generated superior dose–volume parameters and the regression scatterplots in the model converged. This approach could be used to standardize the inverse planning strategies.