评估基于深度学习的目标自动分割技术在磁共振成像引导下的宫颈近距离治疗中的应用

IF 3.4 Q2 ONCOLOGY
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引用次数: 0

摘要

背景和目的宫颈近距离放射治疗的目标结构是由放射肿瘤专家利用成像和临床信息进行分割的。第一次分割时,需要从头开始手工操作。在随后的分次治疗中,第一次分次分割的结果会被硬性传播并进行人工编辑。这一过程非常耗时,而患者只能静静地等待。在这项工作中,我们评估了使用基于人群和患者特异性的自动分割作为第二部分目标分割起点的潜在临床影响。材料和方法对于 28 位接受 MRI 引导近距离放射治疗的局部晚期宫颈癌患者,我们使用两种方法对第二部分图像进行了自动分割:1)基于人群的模型;2)根据第一部分信息微调的患者特定模型。放射肿瘤学家手动编辑自动分割,以评估模型引起的偏差。对自动结构、编辑结构和临床结构进行成对几何和剂量比较。结果编辑后的结构与自动结构的相似程度高于临床结构。编辑结构与临床结构之间的几何和剂量测定差异与文献中研究的观察者间差异相当。与临床工作流程中的手动分割相比,编辑自动分割的速度更快。结论:自动分割会给手动划分带来偏差,但这种偏差与临床无关。自动分区,尤其是针对患者的微调,是一种节省时间的工具,可以改善治疗流程,从而减轻宫颈近距离治疗第二部分的患者负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of deep learning-based target auto-segmentation for Magnetic Resonance Imaging-guided cervix brachytherapy

Background and purpose

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.

Materials and method

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.

Results

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.

Conclusions

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.
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来源期刊
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation
CiteScore
5.30
自引率
18.90%
发文量
93
审稿时长
6 weeks
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