Simões Rita, Rijkmans Eva C, Schaake Eva E, Nowee Marlies E, van der Velden Sandra, Janssen Tomas
Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands (the).
Phys Imaging Radiat Oncol. 2024 Nov 3;32:100669. doi: 10.1016/j.phro.2024.100669. eCollection 2024 Oct.
The target structures for cervix brachytherapy are segmented by radiation oncologists using imaging and clinical information. At the first fraction, this is performed manually from scratch. For subsequent fractions the first fraction segmentations are rigidly propagated and edited manually. This process is time-consuming while patients wait immobilized. In this work, we evaluate the potential clinical impact of using population-based and patient-specific auto-segmentations as a starting point for target segmentation of the second fraction.
For twenty-eight patients with locally advanced cervical cancer, treated with MRI-guided brachytherapy, auto-segmentations were retrospectively generated for the second fraction image using two approaches: 1) population-based model, 2) patient-specific models fine-tuned on first fraction information. A radiation oncologist manually edited the auto-segmentations to assess model-induced bias. Pairwise geometric and dosimetric comparisons were performed for the automatic, edited and clinical structures. The time spent editing the auto-segmentations was compared to the current clinical workflow.
The edited structures were more similar to the automatic than to the clinical structures. The geometric and dosimetric differences between the edited and the clinical structures were comparable to the inter-observer variability investigated in literature. Editing the auto-segmentations was faster than the manual segmentation performed during our clinical workflow. Patient-specific auto-segmentations required less edits than population-based structures.
Auto-segmentation introduces a bias in the manual delineations but this bias is clinically irrelevant. Auto-segmentation, particularly patient-specific fine-tuning, is a time-saving tool that can improve treatment logistics and therefore reduce patient burden during the second fraction of cervix brachytherapy.
宫颈癌近距离放疗的靶区结构由放射肿瘤学家利用影像和临床信息进行分割。在首次分割时，需从头手动完成。对于后续分割，首次分割结果通过刚性配准进行传播并手动编辑。此过程耗时较长，而患者需保持固定不动等待。在本研究中，我们评估了将基于人群和患者特异性的自动分割作为第二次分割靶区分割起点的潜在临床影响。
对于28例接受MRI引导下近距离放疗的局部晚期宫颈癌患者，使用两种方法对第二次分割图像进行回顾性自动分割：1）基于人群的模型；2）根据首次分割信息进行微调的患者特异性模型。放射肿瘤学家手动编辑自动分割结果以评估模型引起的偏差。对自动分割、编辑后的分割及临床分割的结构进行成对的几何和剂量学比较。将编辑自动分割结果所花费的时间与当前临床工作流程进行比较。
编辑后的结构与自动分割结构的相似度高于与临床结构的相似度。编辑后的结构与临床结构之间的几何和剂量学差异与文献中研究的观察者间变异性相当。编辑自动分割结果比我们临床工作流程中的手动分割更快。患者特异性自动分割所需的编辑比基于人群的结构少。
自动分割在手动勾画中引入了偏差，但这种偏差在临床上无关紧要。自动分割，尤其是患者特异性微调，是一种节省时间的工具，可改善治疗流程，从而减轻宫颈癌近距离放疗第二次分割期间的患者负担。
