DVH预测模型在VMAT计划培训中的应用

目的:以宫颈癌为例，探索使用剂量体积直方图（DVH）预测模型培训放疗物理师容积旋转调强（VMAT）计划设计的可行性及其效果。方法:随机选取20例宫颈癌测试病例对3名见习物理师进行两个阶段培训（第一阶段15例，第二阶段5例）。每位物理师分别采用两种方法设计VMAT计划:传统人工计划（MP）和基于DVH预测模型引导的自动计划（KBP）。对比不同阶段两种计划靶区和危及器官的各种剂量学参数，并做相应统计分析。结果:与人工计划相比，自动计划在明显提高PGTV靶区V60覆盖（P<0.001），改善高剂量控制（V66）（P=0.027）的情况下，显著降低膀胱（P<0.001）、直肠（P<0.001）、左右肾（P=0.001和P<0.001）以及左右侧股骨头（P<0.001和P<0.001）等绝大部分正常组织器官的受照剂量。在提高计划合格率的同时，亦减小了计划者间的质量差异。结论:DVH预测模型有助于深化物理师对VMAT初始优化参数设置的理解，加快培训进度，提升VMAT计划设计水平。

Application of DVH prediction model in VMAT planning training

Abstract: Objective To take cervical cancer as an example for exploring the feasibility and outcomes of using dose-volume histogram (DVH) prediction model to train the radiotherapy physicists for volumetric modulated arc therapy (VMAT) planning. Methods Twenty cervical cancer cases were randomly selected to carry out two stages of training (15 cases in the first stage and the other 5 cases in the second stage) for 3 trainee physicists. Each trainee physicist was required to design VMAT plan in two ways, obtaining manual plan (MP) and knowledge-based plan (KBP) which was planned based on DVH prediction model. The dosimetric parameters of target areas and organs-at-risk between two kinds of plans were compared and analyzed. Results Compared with MP, KBP significantly reduced the radiation doses delivered to the most of normal tissues such as bladder (P<0.001), rectum (P<0.001), left and right kidneys (P=0.001, P<0.001), and left and right femoral heads (P<0.001, P<0.001), while improving the V60 of PGTV (P<0.001) and enhancing high dose control (V66) (P=0.027). The KBP based on DVH prediction model not only increased the passing rate of VMAT plan, but also minimized inter-planner diversity. Conclusion DVH prediction model helps to deepen the physicists understanding on the setting of the initial optimization parameters of VMAT plan, speed up the training schedule and improve the VMAT planning skills.