Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy.
School of Engineering, Cardiff University, Cardiff, United Kingdom.
Phys Med Biol. 2021 Jun 23;66(13). doi: 10.1088/1361-6560/ac08b0.
This study aimed to investigate if a commercial, knowledge-based tool for radiotherapy planning could be used to estimate the amount of sparing in organs at risk (OARs) in the re-planning strategy for adaptive radiotherapy (ART). Eighty head and neck (HN) VMAT Pareto plans from our institute's database were used to train a knowledge-based planning (KBP) model. An evaluation set of another 20 HN patients was randomly selected. For each patient in the evaluation set, the planning computed tomography (CT) and 2 sets of on-board cone-beam CT, corresponding to the middle and second half of the radiotherapy treatment course, were extracted. The original plan was re-calculated on a daily deformed CT (delivered dose-volume histogram (DVH)) and compared with the KBP DVH predictions and with the final KBP DVH after optimisation of the plan, which was performed on the same image sets. To evaluate the feasibility of this method, the range of KBP DVH uncertainties was compared with the gains obtained from re-planning. DVH differences and receiver operating characteristic (ROC) curve analysis were used for this purpose. On average, final KBP uncertainties were smaller than the gain in re-planning. Statistical tests confirmed significant differences between the two groups. ROC analysis showed KBP performance in terms of area under the curve values higher than 0.7, which confirmed a good accuracy in predicted values. Overall, for 48% of cases, KBP predicted a desirable outcome from re-planning, and the final dose confirmed an effective gain in 47% of cases. We have established a systematic workflow to identify effective OAR sparing in re-planning based on KBP predictions that can be implemented in an on-line, ART process.
本研究旨在探讨一种商业化的、基于知识的放射治疗计划工具是否可用于估计自适应放射治疗（ART）再计划策略中危及器官（OAR）的受量减少量。从我们研究所的数据库中使用 80 个头颈部（HN）VMAT Pareto 计划来训练基于知识的计划（KBP）模型。随机选择另 20 个 HN 患者的评估集。对于评估集中的每个患者，提取计划计算机断层扫描（CT）和 2 套在线锥形束 CT，分别对应放射治疗过程的中间和后半部分。原始计划在每日变形 CT（传递剂量体积直方图（DVH））上重新计算，并与 KBP DVH 预测值以及在同一图像集上进行的计划优化后的最终 KBP DVH 进行比较。为了评估这种方法的可行性，将 KBP DVH 不确定性范围与从再计划中获得的收益进行了比较。为此目的使用了 DVH 差异和接收者操作特性（ROC）曲线分析。平均而言，最终 KBP 不确定性小于再计划中的增益。统计检验证实了两组之间的显著差异。ROC 分析表明，KBP 性能的曲线下面积值高于 0.7，这证实了预测值的良好准确性。总体而言，对于 48%的病例，KBP 预测了再计划的理想结果，最终剂量证实了 47%的病例有效增益。我们已经建立了一个系统的工作流程，该流程基于 KBP 预测来识别再计划中 OAR 受量减少的有效性，可在在线、ART 过程中实施。
