Knowledge-based semi-automated optimization of intensity-modulated radiotherapy plans using a volume modulated arc therapy-configured model / 中华放射肿瘤学杂志

ABSTRACT

Objective To evaluate the feasibility and dosimetric features of a volume modulated arc therapy (VMAT)-configured model in knowledge-based optimization of fixed-field dynamic intensitymodulated radiotherapy (IMRT) plans based on the Varian RapidPlan system.Methods ① A dose-volume histogram prediction model was trained with 81 qualified preoperative VMAT plans for rectal cancer and then statistically verified.② For clinically approved 10 IMRT plans with the same dose prescription,the above model was used to automatically generate new optimization parameters and dynamic muhileaf collimator (MLC) sequences with field geometry and beam energy unchanged.③ In order to rule out the disparities between different versions,a single algorithm was used to calculate the absolute doses of the original and new plans.④ Statistical analyses were performed on dosimetric parameters after comparable target dose coverage was achieved in the two plans by appropriate normalization.Results On the basis of similar target dose homogeneity and coverage,RapidPlan significantly reduced the doses to the urinary bladder (D50％ by 9.01 Gy,P =0.000;Dmean by 8.08 Gy,P =0.005) and the femoral head (D50％ by 4.20 Gy,P =0.000;Dmean by 3.84 Gy,P=0.005) but significantly elevated the mean total number of monitor units (1211±99 vs.771±79,P=0.000) and the number of fields with multiple MLC carriage groups.The knowledge-based semi-automated optimization caused a significantly larger number of high-dose hotspots but a similar D2％ (52.54 vs.52.71 Gy,P=0.239).Conclusions The VMAT model can be used for the knowledge-based semi-automated optimization of IMRT plans to enhance the efficiency and OAR protection.However,the resulting high-dose hotspots need further manual intervention.