Zhong Yang, Yu Lei, Zhao Jun, Fang Yingtao, Yang Yanju, Wu Zhiqiang, Wang Jiazhou, Hu Weigang
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Front Oncol. 2021 Jun 24;11:697995. doi: 10.3389/fonc.2021.697995. eCollection 2021.
This study aims to demonstrate the feasibility of clinical implementation of automated treatment planning (ATP) using voxel-based dose prediction and post-optimization strategies for rectal cancer on uRT (United Imaging Healthcare, Shanghai, China) treatment planning system.
A total of 180 previously treated rectal cancer cases were enrolled in this study, including 160 cases for training, 10 for validation and 10 for testing. Using CT image data, planning target volumes (PTVs) and contour delineation of the organs at risk (OARs) as input and three-dimensional (3D) dose distribution as output, a 3D-Uet DL model was developed. Based on the voxel-wise prediction dose distribution, intensity-modulated radiation therapy (IMRT) plans were then generated automatedly using post-optimization strategies, including a complex clinical dose target metrics homogeneity index (HI) and conformation index (CI). To evaluate the performance of the proposed ATP approach, the dose-volume histogram (DVH) parameters of OARs and PTV and the 3D dose distributions of the plan were compared with those of manual plans.
By combining clinical post-optimization strategies, the automatically generated treatment plan can achieve better homogeneous PTV coverage and dose sparing for OARs except the mean dose for femoral-head compared with the use of the mean square error objective function alone. Compared with the manual plan, no statistically significant differences in HI, CI or global maximum dose were found. The manual plans perform slightly better than plans with post-optimization strategies in other dosimetric indexes, but these plans are still within clinical requirements.
With the help of clinical post-optimization strategies, the proposed new ATP solution can generate IMRT plans that are within clinically acceptable levels and comparable to plans manually generated by dosimetrists.
本研究旨在证明在联影医疗（中国上海）治疗计划系统上，使用基于体素的剂量预测和直肠癌调强放疗（IMRT）计划的优化后策略实现自动治疗计划（ATP）临床应用的可行性。
本研究共纳入180例既往治疗过的直肠癌病例，其中160例用于训练，10例用于验证，10例用于测试。以CT图像数据、计划靶区（PTV）和危及器官（OAR）的轮廓勾画作为输入，三维（3D）剂量分布作为输出，开发了一种3D-Uet深度学习模型。基于体素预测剂量分布，然后使用包括复杂临床剂量目标指标均匀性指数（HI）和适形指数（CI）在内的优化后策略自动生成IMRT计划。为了评估所提出的ATP方法的性能，将OAR和PTV的剂量体积直方图（DVH）参数以及计划的3D剂量分布与手动计划进行比较。
与仅使用均方误差目标函数相比，通过结合临床优化后策略，自动生成的治疗计划可以实现更好的PTV均匀覆盖和OAR剂量 sparing，但股骨头平均剂量除外。与手动计划相比，在HI、CI或全局最大剂量方面未发现统计学显著差异。在其他剂量学指标方面，手动计划的表现略优于采用优化后策略的计划，但这些计划仍在临床要求范围内。
借助临床优化后策略，所提出的新ATP解决方案可以生成临床可接受水平的IMRT计划，并且与剂量师手动生成的计划相当。
