Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
Radiother Oncol. 2023 May;182:109518. doi: 10.1016/j.radonc.2023.109518. Epub 2023 Feb 2.
Image-guided adaptive brachytherapy (IGABT) is an important modality in the cervical cancer treatment, and plan quality is sensitive to time pressure in the workflow. Patient anatomy-based quality-assurance (QA) with overlap volume histograms (OVHs) has been demonstrated to detect suboptimal plans (outliers). This analysis quantifies the possible improvement of plans detected as outliers, and investigates its suitability as a clinical QA tool in a multi-center setting.
In previous work OVH-based models were investigated for the use of QA. In this work a total of 160 plans of 68 patients treated in accordance with the current state-of-the-art IGABT protocol from Erasmus MC (EMC) were analyzed, with a model based on 120 plans (60 patients) from UMC Utrecht (UMCU). Machine-learning models were trained to define QA thresholds, and to predict dose D to bladder, rectum, sigmoid and small bowel with the help of OVHs of the EMC cohort. Plans out of set thresholds (outliers) were investigated and retrospectively replanned based on predicted D values.
Analysis of replanned plans demonstrated a median improvement of 0.62 Gy for all Organs At Risk (OARs) combined and an improvement for 96 % of all replanned plans. Outlier status was resolved for 36 % of the replanned plans. The majority of the plans that could not be replanned were reported having implantation complications or insufficient coverage due to tumor geometry.
OVH-based QA models can detect suboptimal plans, including both unproblematic BT applications and suboptimal planning circumstances in general. OVH-based QA models demonstrate potential for clinical use in terms of performance and user-friendliness, and could be used for knowledge transfer between institutes. Further research is necessary to differentiate between (sub)optimal planning circumstances.
图像引导自适应近距离放射治疗（IGABT）是宫颈癌治疗的重要手段，而计划质量对工作流程中的时间压力较为敏感。基于患者解剖结构的质量保证（QA）与重叠体积直方图（OVH）相结合，已被证明可检测到不理想的计划（离群值）。本分析量化了检测为离群值的计划可能的改进，并研究了其作为多中心环境中临床 QA 工具的适用性。
在之前的工作中，已经研究了基于 OVH 的模型在 QA 中的应用。在这项工作中，分析了总共 160 例来自 68 名患者的计划，这些患者按照当前的 Erasmus MC（EMC）IGABT 协议进行治疗，其中有 60 例患者（60 例）来自 UMC Utrecht（UMCU）的模型。使用 EMC 队列的 OVH 训练机器学习模型来定义 QA 阈值，并预测膀胱、直肠、乙状结肠和小肠的剂量 D。研究超出设定阈值的计划（离群值），并根据预测的 D 值进行回顾性重新计划。
对重新计划的计划进行分析表明，所有危及器官（OAR）的中位数改善了 0.62Gy，并且改善了 96%的重新计划的计划。36%的重新计划的计划的离群值状态得到解决。无法重新计划的计划大多数是由于肿瘤几何形状导致的植入并发症或覆盖不足。
基于 OVH 的 QA 模型可以检测到不理想的计划，包括一般来说没有问题的 BT 应用和不理想的计划情况。基于 OVH 的 QA 模型在性能和用户友好性方面具有潜在的临床应用价值，并可用于机构之间的知识转移。需要进一步研究以区分（次）优的计划情况。
