Zhang Fuli, Wang Qiusheng, Lu Na, Chen Diandian, Jiang Huayong, Yang Anning, Yu Yanjun, Wang Yadi
Radiation Oncology Department, The Seventh Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
Front Oncol. 2023 Jul 18;13:1174530. doi: 10.3389/fonc.2023.1174530. eCollection 2023.
To introduce a model for automatic segmentation of thoracic organs at risk (OARs), especially the esophagus, in non-small cell lung cancer radiotherapy, using a novel two-step deep learning network.
A total of 59 lung cancer patients' CT images were enrolled, of which 39 patients were randomly selected as the training set, 8 patients as the validation set, and 12 patients as the testing set. The automatic segmentations of the six OARs including the esophagus were carried out. In addition, two sets of treatment plans were made on the basis of the manually delineated tumor and OARs (Plan1) as well as the manually delineated tumor and the automatically delineated OARs (Plan2). The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) of the proposed model were compared with those of U-Net as a benchmark. Next, two groups of plans were also compared according to the dose-volume histogram parameters.
The DSC, HD95, and ASD of the proposed model were better than those of U-Net, while the two groups of plans were almost the same. The highest mean DSC of the proposed method was 0.94 for the left lung, and the lowest HD95 and ASD were 3.78 and 1.16 mm for the trachea, respectively. Moreover, the DSC reached 0.73 for the esophagus.
The two-step segmentation method can accurately segment the OARs of lung cancer. The mean DSC of the esophagus realized preliminary clinical significance (>0.70). Choosing different deep learning networks based on different characteristics of organs offers a new option for automatic segmentation in radiotherapy.
介绍一种使用新型两步深度学习网络对非小细胞肺癌放疗中胸部危及器官（OARs），尤其是食管进行自动分割的模型。
共纳入59例肺癌患者的CT图像，其中39例患者被随机选为训练集，8例患者为验证集，12例患者为测试集。对包括食管在内的六个OARs进行自动分割。此外，基于手动勾画的肿瘤和OARs制定了两组治疗计划（计划1）以及基于手动勾画的肿瘤和自动勾画的OARs制定了两组治疗计划（计划2）。将所提模型的骰子相似系数（DSC）、95%豪斯多夫距离（HD95）和平均表面距离（ASD）与作为基准的U-Net模型进行比较。接下来，还根据剂量体积直方图参数对两组计划进行了比较。
所提模型的DSC、HD95和ASD均优于U-Net模型，而两组计划几乎相同。所提方法的最高平均DSC，左肺为0.94，气管的最低HD95和ASD分别为3.78和1.16毫米。此外，食管的DSC达到0.73。
两步分割法可准确分割肺癌的OARs。食管的平均DSC达到了初步临床意义（>0.70）。根据器官的不同特征选择不同的深度学习网络为放疗中的自动分割提供了新的选择。
