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The aim of this work was to demonstrate a practical and effective method to improve the performance of RapidPlan (RP) model. 203 consecutive clinical VMAT plans (P 0 ) for cervical and endometrial cancer were used to train an RP model (M 0 ). The plans were then reoptimized by M 0  to generate 203 new plans (P 1 ). Compared with P 0 , 150 plans with a lower mean dose (MD) of bladder, rectum and PBM were selected from P 1  to configure a new RP model (M 1 ). A final RP model (M 2 ) was trained using plans in M 1  and the remaining 53 plans from P 1  (excluding OARs with worse MD) and the corresponding plans from P 0  (only including OARs with better MD). The models were validated on the mentioned 53 plans (closed-loop set) and 46 patient cohorts outside the training library (open-loop set).  p  &lt; 0.05 was considered statistically significant. For closed-loop validation, the difference of D 2% , D 98%  and CI 95%  between groups was of no statistical significance, the homogeneity index (HI) was lower in the groups of RP models ( p  &lt; 0.05). The MD of all OARs decreased monotonically in the sequence of the clinical group, group M 0 , M 1  and M 2 , except the MD of bowel in M 1  and MD of LFH in M 2 . Similarly, for open-loop validation, there was no significant difference in D 2% , D 98%  and HI between groups, but CI 95%  was larger in the clinical group ( p  &lt; 0.05). The MD of all OARs decreased monotonically in the sequence of the clinical group, group M 0 , M 1  and M 2 , with the exception of bowel in M 1 . The practical method of incorporating plan data of better-sparing OARs from both the clinical VMAT plans and the re-optimized plans could further improve the performance of the RP model.

A practical method to improve the performance of knowledge-based VMAT planning for endometrial and cervical cancer