UNASSIGNED:食管鳞状细胞癌(ESCC)的大体肿瘤体积(GTV)的准确定义可以促进精确的照射场测定,并进一步达到放疗的疗效。这项回顾性研究旨在评估利用基于深度学习的方法从诊断为ESCC的患者的3D18F-FDGPET/CT图像自动定义GTV的适用性。
UNASSIGNED:我们对临床队列进行了164次18F-FDGPET/CT扫描实验。首先采用最先进的食管GTV分割深度神经网络来描绘PET/CT图像上的病变区域。之后,我们提出了一种新颖的等效截顶椭锥积分法(ETECIM)来估计GTV值。骰子相似系数(DSC)指标,Hausdorff距离(HD),和平均表面距离(MSD)用于评估分割性能。符合性指数(CI),包容度(DI),和运动矢量(MV)用于评估预测和地面实况肿瘤之间的差异。GTV的统计差异,DI,位置也是确定的。
UNASSIGNED:我们进行4倍交叉验证以进行评估,报告DSC的值,HD,MSD为0.72±0.02,11.87±4.20mm,和2.43±0.60mm(平均值±标准偏差),分别。对于每个倍数交叉验证,皮尔逊相关性(R2)达到0.8434、0.8004、0.9239和0.7119,预测和地面真值GTT之间没有显着差异(t=1.193,p=0.235)。对于DI,发现显著差异(t=-2.263,p=0.009)。对于职位评估,预测和地面真实GTT之间没有显着差异(x方向的左右:t=0.102,p=0.919,y方向的前后:t=0.221,p=0.826,z方向的颅尾:t=0.569,p=0.570)。CI的中位数为0.63,得到的MV很小。
UNASSIGNED:预测的肿瘤与手动地面实况非常吻合。所提出的GTV估计方法ETECIM比最常用的体素体积求和方法更精确。由于与预测结果具有良好的线性相关性,因此可以求解出地面实况GTT。基于深度学习的方法在GTV定义和临床放射治疗中显示出其广阔的应用前景。
UNASSIGNED: The accurate definition of gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) can promote precise irradiation field determination, and further achieve the radiotherapy curative effect. This retrospective study is intended to assess the applicability of leveraging deep learning-based method to automatically define the GTV from 3D 18F-FDG PET/CT images of patients diagnosed with ESCC.
UNASSIGNED: We perform experiments on a clinical cohort with 164 18F-FDG PET/CT scans. The state-of-the-art esophageal GTV segmentation deep neural net is first employed to delineate the lesion area on PET/CT images. Afterwards, we propose a novel equivalent truncated elliptical cone integral method (ETECIM) to estimate the GTV value. Indexes of Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD) are used to evaluate the segmentation performance. Conformity index (CI), degree of inclusion (DI), and motion vector (MV) are used to assess the differences between predicted and ground truth tumors. Statistical differences in the GTV, DI, and position are also determined.
UNASSIGNED: We perform 4-fold cross-validation for evaluation, reporting the values of DSC, HD, and MSD as 0.72 ± 0.02, 11.87 ± 4.20 mm, and 2.43 ± 0.60 mm (mean ± standard deviation), respectively. Pearson correlations (R2) achieve 0.8434, 0.8004, 0.9239, and 0.7119 for each fold cross-validation, and there is no significant difference (t = 1.193, p = 0.235) between the predicted and ground truth GTVs. For DI, a significant difference is found (t = -2.263, p = 0.009). For position assessment, there is no significant difference (left-right in x direction: t = 0.102, p = 0.919, anterior-posterior in y direction: t = 0.221, p = 0.826, and cranial-caudal in z direction: t = 0.569, p = 0.570) between the predicted and ground truth GTVs. The median of CI is 0.63, and the gotten MV is small.
UNASSIGNED: The predicted tumors correspond well with the manual ground truth. The proposed GTV estimation approach ETECIM is more precise than the most commonly used voxel volume summation method. The ground truth GTVs can be solved out due to the good linear correlation with the predicted results. Deep learning-based method shows its promising in GTV definition and clinical radiotherapy application.