Department of Nuclear Medicine, Radiotherapy & Oncology, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia.
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, Guangdong, China.
Eur J Med Res. 2023 Aug 31;28(1):309. doi: 10.1186/s40001-023-01278-1.
The aim of this study was to investigate the feasibility of VMAT library-derived model transfer in the prediction of IMRT plans by dosimetry comparison among with three groups of IMRT plans: two groups of automatic IMRT plans generated by the knowledge-based the volumetric modulated arc therapy (VMAT) model and intensity-modulated radiation therapy (IMRT) model and one group of manual IMRT plans.
52 prostate cancer patients who had completed radiotherapy were selected and randomly divided into 2 groups with 40 and 12 separately. Then both VMAT and IMRT plans were manually designed for all patients. The total plans in the group with 40 cases as training datasets were added to the knowledge-based planning (KBP) models for learning and finally obtained VMAT and IMRT training models. Another 12 cases were selected as the validation group to be used to generated auto IMRT plans by KBP VMAT and IMRT models. At last, the radiotherapy plans from three groups were obtained: the automated IMRT plan (V-IMRT) predicted by the VMAT model, the automated IMRT plan (I-IMRT) predicted by the IMRT model and the manual IMRT plan (M-IMRT) designed before. The dosimetric parameters of planning target volume (PTV) and organ at risks (OARs) as well as the time parameters (monitor unit, MU) were statistically analyzed.
The dose limit of all plans in the training datasets met the clinical requirements. Compared with the training plans added to VMAT model, the dosimetry parameters have no statistical differences in PTV (P > 0.05); the dose of X% volume (Dx%) with D25% and D35% in rectal and the maximum dose (Dmax) in the right femoral head were lower (P = 0.04, P = 0.01, P = 0.00) while D50% in rectal was higher (< 0.05) in the IMRT model plans. In the 12 validation cases, both automated plans showed better dose distribution compared with the M-IMRT plan: the Dmax of PTV in the I-IMRT plans and the dose in volume of interesting (VOI) of bladder and bilateral femoral heads were lower with a statistically significant difference (P < 0.05). Compared with the I-IMRT plans, dosimetric parameters in PTV and VOI of all OARs had no statistically significant differences (P > 0.05), but the Dmax in left femoral heard and D15% in the right femoral head were lower and have significant differences (P < 0.05). Furthermore, the low-dose regions, which was defined as all volumes outside of the PTV (RV) with the statistical parameters of mean dose (Dmean), the volume of covering more than 5 Gy dose (V5Gy), and also the time parameter (MU) required to perform the plan were considered. The results showed that Dmean in V-IMRT was smaller than that in the I-IMRT plan (P = 0.02) and there was no significant difference in V5Gy and MU (P > 0.05).
Compared with the manual plan, the IMRT plans generated by the KBP models had a significant advantage in dose control of both OARs and PTV. Compared to the I-IMRT plans, the V-IMRT plans was not only without significant disadvantages, but it also achieved slightly better control of the low-dose region, which meet the clinical requirements and can used in the clinical treatment. This study demonstrates that it is feasible to transfer the KBP VMAT model in the prediction of IMRT plans.
本研究旨在通过比较三组调强放疗（IMRT）计划的剂量学，探讨容积调强弧形治疗（VMAT）库衍生模型转移在预测调强放疗计划中的可行性。这三组计划分别为：两组由基于知识的容积调强弧形治疗（VMAT）模型和调强放疗（IMRT）模型自动生成的 IMRT 计划，以及一组手动设计的 IMRT 计划。
选择 52 例已完成放疗的前列腺癌患者，随机分为两组，每组 40 例和 12 例。然后为所有患者手动设计总计划。将 40 例患者的总计划作为训练数据集添加到基于知识的规划（KBP）模型中进行学习，最终获得 VMAT 和 IMRT 训练模型。另选 12 例作为验证组，用于通过 KBP VMAT 和 IMRT 模型生成自动 IMRT 计划。最后，获得三组放疗计划：由 VMAT 模型预测的自动 IMRT 计划（V-IMRT）、由 IMRT 模型预测的自动 IMRT 计划（I-IMRT）和手动设计的 IMRT 计划（M-IMRT）。统计分析计划靶区（PTV）和危及器官（OARs）的剂量学参数以及时间参数（监测单位，MU）。
训练数据集的所有计划的剂量限制均符合临床要求。与添加到 VMAT 模型的训练计划相比，PTV 的剂量学参数无统计学差异（P>0.05）；直肠的 X%体积（Dx%）、D25%和 D35%剂量以及右侧股骨头的最大剂量（Dmax）较低（P=0.04、P=0.01、P=0.00），而直肠的 D50%较高（P<0.05）。在 12 例验证病例中，与 M-IMRT 计划相比，两种自动计划的剂量分布均更好：I-IMRT 计划的 PTV Dmax 以及膀胱和双侧股骨头的感兴趣体积（VOI）剂量较低，差异有统计学意义（P<0.05）。与 I-IMRT 计划相比，所有 OARs 的 PTV 和 VOI 的剂量学参数无统计学差异（P>0.05），但左侧股骨头的 Dmax 和右侧股骨头的 D15%较低，差异有统计学意义（P<0.05）。此外，定义为 PTV 外所有体积（RV）的统计参数，包括平均剂量（Dmean）、覆盖超过 5Gy 剂量的体积（V5Gy）以及执行计划所需的时间参数（MU）的低剂量区。结果表明，V-IMRT 的 Dmean 小于 I-IMRT 计划（P=0.02），V5Gy 和 MU 无统计学差异（P>0.05）。
与手动计划相比，KBP 模型生成的 IMRT 计划在 OAR 和 PTV 的剂量控制方面具有显著优势。与 I-IMRT 计划相比，V-IMRT 计划不仅没有明显的劣势，而且还能更好地控制低剂量区，符合临床要求，可用于临床治疗。本研究表明，在预测调强放疗计划中，转移 KBP VMAT 模型是可行的。
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