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

Abstract

Purpose The aim of this work was to demonstrate a practical and effective method to improve the performance of RapidPlan (RP) model. Methods 203 consecutive clinical VMAT plans (P0) for cervical and endometrial cancer were used to train an RP model (M0). The plans were then reoptimized by M0 to generate 203 new plans (P1). Compared with P0, 150 plans with a lower mean dose (MD) of bladder, rectum and PBM were selected from P1 to configure a new RP model (M1). A final RP model (M2) was trained using plans in M1 and the remaining 53 plans from P1 (excluding OARs with worse MD) and the corresponding plans from P0 (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 < 0.05 was considered statistically significant. Results For closed-loop validation, the difference of D2%, D98% and CI95% between groups was of no statistical significance, the homogeneity index (HI) was lower in the groups of RP models (p < 0.05). The MD of all OARs decreased monotonically in the sequence of the clinical group, group M0, M1 and M2, except the MD of bowel in M1 and MD of LFH in M2. Similarly, for open-loop validation, there was no significant difference in D2%, D98% and HI between groups, but CI95% was larger in the clinical group (p < 0.05). The MD of all OARs decreased monotonically in the sequence of the clinical group, group M0, M1 and M2, with the exception of bowel in M1. Conclusion 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.