Applying a novel two-step deep learning network to improve the automatic delineation of esophagus in non-small cell lung cancer radiotherapy - Zhang, Fuli; Wang, Qiusheng; Lu, Na; Chen, Diandian; Jiang, Huayong; Yang, Anning; Yu, Yanjun; Wang, Yadi (Frontiers in Oncology, 2023)
Purpose: To introduce a model for automatic segmentation of thoracic OARs, especially the esophagus in non-small cell lung cancer radiotherapy, using a novel two-step deep learning network. Materials and Methods: Fifty-nine 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 organs at risk (OARs) including 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 (DVH) parameters. Results: 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 mm and 1.16 mm for the trachea, respectively. Moreover, the DSC reached 0.73 for the esophagus. Conclusions: 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.