利用VMAT模型基于知识IMRT计划半自动优化

Knowledge-based semi-automated optimization of intensity-modulated radiotherapy plans using a volume modulated arc therapy-configured model

目的利用RapidPlan系统评估VMAT模型用于基于知识优化固定野动态IMRT计划可行性及剂量学特点。方法①利用81例优质直肠癌术前同步推量VMAT计划训练DVH预测模型并进行统计学验证：②复制经临床确认的10例相同处方IMRT计划，保持布野及能量等参数不变，用上述模型自动生成新的优化参数及各野动态MLC序列；③利用相同剂量计算方法计算原计划及新计划的绝对剂量以排除版本间差异；④将计划归一至相近靶区剂量覆盖后对各剂量学参数行统计学分析。结果在相似靶区剂量均匀性及覆盖率基础上，RapidPlan计划显著且大幅减低了膀胱受量[D50%降低9．01Gy（P=0．000），Dmean降低8．08Gy（P=0．005）]和股骨头受量[D50%降低4．20Gy（P=0．000），Dmean降低3．84Gy（P=0．005）]，但平均总跳数也显著高于原计划[（1211±99）MU比（771±79）MU，P=0．000]且劈野数更多。基于知识的半自动优化导致高剂量热区明显增加，但D2%相近（52．54Gy：52．71Gy，P=0．239）。结论VMAT模型可用于基于知识IMRT半自动优化以提高效率并改善OAR保护，但高剂量热区需进一步人工干预。

Objective To evaluate the feasibility and dosimetfic features of a volume modulated arc therapy （VMAT）-configured model in knowledge-based optimization of fixed-field dynamic intensity- modulated radiotherapy （IMRT） plans based on the Vafian 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 multileaf 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;D by 8.08 Gy, P= 0. 005） and the femoral head （D50%, by 4. 20 Gy, P= 0. 000; D 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.