三期相CT图像分割模型的鼻咽癌肿瘤靶区自动勾画研究

目的 探究使用基于3D U-Net结合三期相CT图像的分割模型对鼻咽癌肿瘤原发灶(GTVnx)和转移的区域淋巴结(GTVnd)自动勾画的有效性和可行性。方法 回顾性收集215例鼻咽癌病例的电子计算机体层扫描(CT),包括平扫期(CT)、增强期(CTC)和延迟期(CTD)3个期相共计645组图像。采用随机数字表法,将数据集划分为172例训练集和43例的测试集。设置了包括三期相CT图像模型及期相微调模型共计6个实验组:三期相CT图像模型即仅使用平扫期(CT)A1组、仅使用增强期(CTC)A2组、仅使用延迟期(CTD)A3组和同时使用三期相(All)A4组。期相微调模型:CTC微调B1组和CTD微调B2组。使用Dice相似性系数(DSC)和95%豪斯多夫距离(HD95)作为定量评价指标。结果 使用三期相CT(A4)进行GTVnd靶区自动勾画相比于仅使用单期相CT(A1、A2、A3)获得更好的勾画效果(DSC:0.67 vs. 0.61、0.64、0.64, t=7.48、3.27、4.84,P<0.01; HD95: 36.45 mm vs. 79.23、59.55、65.17 mm,t=5.24、2.99、3.89,P<0.01),差异有统计学意义。使用三期相CT(A4)对于GTVnx的自动勾画效果相比于仅使用单期相(A1、A2、A3)无明显提升(DSC: 0.73 vs. 0.74、0.74、0.74;HD95: 14.17 mm vs. 8.06、8.11、8.10 mm),差异无统计学意义(P>0.05)。在GTVnd的自动勾画中,B2、B3 vs. A1模型具有更好的自动勾画精度(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)。结论 使用三期CT扫描对于鼻咽癌GTVnd靶区具有更好的自动勾画效果。通过期相微调模型,可以提升平扫CT图像上GTVnd靶区的自动勾画精度。

Application of a deep learning-based three-phase CT image models for the automatic segmentation of gross tumor volumes in nasopharyngeal carcinoma

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.