| Yao Guorong,Shen Kai,Zhao Feng,Wang Siyuan,Lu Zhongjie,Huang Kejie,Yan Senxiang.Application of a deep learning-based three-phase CT image models for the automatic segmentation of gross tumor volumes in nasopharyngeal carcinoma[J].Chinese Journal of Radiological Medicine and Protection,2024,44(2):111-118 |
| Application of a deep learning-based three-phase CT image models for the automatic segmentation of gross tumor volumes in nasopharyngeal carcinoma |
| Received:May 09, 2023 |
| DOI：10.3760/cma.j.cn112271-20230509-00139 |
| KeyWords:Nasopharyngeal carcinoma Automatic segmentation Radiotherapy Deep learning Convolutional neural network |
| FundProject:浙江省重点研发计划(2021C03122) |
| Author Name | Affiliation | E-mail | | Yao Guorong | Department of Radiation Oncology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China | | | Shen Kai | College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310058, China | | | Zhao Feng | Department of Radiation Oncology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China | | | Wang Siyuan | Department of Radiation Oncology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China | | | Lu Zhongjie | Department of Radiation Oncology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China | | | Huang Kejie | College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310058, China | | | Yan Senxiang | Department of Radiation Oncology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China | yansenxiang@zju.edu.cn |
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| Abstract:: |
| Objective To investigate the effectiveness and feasibility of a 3D U-Net in conjunction with a three-phase CT image segmentation model in the automatic segmentation of GTVnx and GTVnd in nasopharyngeal carcinoma. Methods A total of 645 sets of computed tomography (CT) images were retrospectively collected from 215 nasopharyngeal carcinoma cases, including three phases: plain scan (CT), contrast-enhanced CT (CTC), and delayed CT (CTD). The dataset was grouped into a training set consisting of 172 cases and a test set comprising 43 cases using the random number table method. Meanwhile, six experimental groups, A1, A2, A3, A4, B1, and B2, were established. Among them, the former four groups used only CT, only CTC, only CTD, and all three phases, respectively. The B1 and B2 groups used phase fine-tuning CTC models. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) served as quantitative evaluation indicators. Results Compared to only monophasic CT (group A1/A2/A3), triphasic CT (group A4) yielded better result in the automatic segmentation of GTVnd (DSC: 0.67 vs. 0.61, 0.64, 0.64; t = 7.48, 3.27, 4.84, P < 0.01; HD95: 36.45 vs. 79.23, 59.55, 65.17; t = 5.24, 2.99, 3.89, P < 0.01), with statistically significant differences (P < 0.01). However, triphasic CT (group A4) showed no significant enhancement in the automatic segmentation of GTVnx compared to monophasic CT (group A1/A2/A3) (DSC: 0.73 vs. 0.74, 0.74, 0.73; HD95: 14.17 mm vs. 8.06, 8.11, 8.10 mm), with no statistically significant difference (P > 0.05). For the automatic segmentation of GTVnd, group B1/B2 showed higher automatic segmentation accuracy compared to group A1 (DSC: 0.63, 0.63 vs. 0.61, t = 4.10, 3.03, P<0.01; HD95: 58.11, 50.31 mm vs. 79.23 mm, t = 2.75, 3.10, P < 0.01). Conclusions Triphasic CT scanning can improve the automatic segmentation of the GTVnd in nasopharyngeal carcinoma. Additionally, phase fine-tuning models can enhance the automatic segmentation accuracy of the GTVnd on plain CT images. |
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