KU Leuven - University of Leuven, Department of Oncology, Experimental Radiation Oncology, Belgium; University Hospitals Leuven, Department of Radiation Oncology, Belgium.
Medical Imaging Research Center, University Hospitals Leuven, Belgium; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Belgium.
Radiother Oncol. 2022 Jun;171:84-90. doi: 10.1016/j.radonc.2022.04.015. Epub 2022 Apr 18.
PURPOSE/OBJECTIVE(S): Precise segmentation of clinical target volumes (CTV) in breast cancer is indispensable for state-of-the art radiotherapy. Despite international guidelines, significant intra- and interobserver variability exists, negatively impacting treatment outcomes. The aim of this study is to evaluate the performance and efficiency of segmentation of CTVs in planning CT images of breast cancer patients using a 3D convolutional neural network (CNN) compared to the manual process.
MATERIALS/METHODS: An expert radiation oncologist (RO) segmented all CTVs separately according to international guidelines in 150 breast cancer patients. This data was used to create, train and validate a 3D CNN. The network's performance was additionally evaluated in a test set of 20 patients. Primary endpoints are quantitative and qualitative analysis of the segmentation data generated by the CNN for each level specifically as well as for the total PTV to be irradiated. The secondary endpoint is the evaluation of time efficiency.
In the test set, segmentation performance was best for the contralateral breast and the breast CTV and worst for Rotter's space and the internal mammary nodal (IMN) level. Analysis of impact on PTV resulted in non-significant over-segmentation of the primary PTV and significant under-segmentation of the nodal PTV, resulting in slight variations of overlap with OARs. Guideline consistency improved from 77.14% to 90.71% in favor of CNN segmentation while saving on average 24 minutes per patient with a median time of 35 minutes for pure manual segmentation.
3D CNN based delineation for breast cancer radiotherapy is feasible and performant, as scored by quantitative and qualitative metrics.
精确勾画乳腺癌的临床靶区（CTV）对于先进的放疗至关重要。尽管有国际指南，但仍存在显著的观察者内和观察者间差异，这对治疗结果产生负面影响。本研究旨在评估使用 3D 卷积神经网络（CNN）与手动过程相比，在乳腺癌患者的计划 CT 图像中分割 CTV 的性能和效率。
材料/方法：一位经验丰富的放射肿瘤学家（RO）根据国际指南，在 150 例乳腺癌患者中分别对所有 CTV 进行了手动分割。该数据用于创建、训练和验证 3D CNN。该网络的性能还在另外 20 例患者的测试集中进行了评估。主要终点是对 CNN 为每个特定水平以及要照射的总 PTV 生成的分割数据进行定量和定性分析。次要终点是评估时间效率。
在测试集中，该网络在对侧乳房和乳房 CTV 的分割性能最好，在对 Rotter 间隙和内乳淋巴结（IMN）水平的分割性能最差。对 PTV 的影响分析导致原发性 PTV 的过度分割和淋巴结 PTV 的明显欠分割，导致与 OAR 的重叠略有变化。指南一致性从 77.14%提高到 90.71%，有利于 CNN 分割，同时平均每位患者节省 24 分钟，纯手动分割的中位数时间为 35 分钟。
基于 3D CNN 的乳腺癌放疗勾画是可行且高效的，可通过定量和定性指标进行评分。
