Duan Jinghao, Qiu Qingtao, Zhu Jian, Shang Dongping, Dou Xue, Sun Tao, Yin Yong, Meng Xiangjuan
School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin, China.
Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Front Oncol. 2022 Apr 14;12:881931. doi: 10.3389/fonc.2022.881931. eCollection 2022.
Accurate lesion segmentation is a prerequisite for radiomic feature extraction. It helps to reduce the features variability so as to improve the reporting quality of radiomics study. In this research, we aimed to conduct a radiomic feature reproducibility test of inter-/intra-observer delineation variability in hepatocellular carcinoma using 3D-CT images, 4D-CT images and multiple-parameter MR images.
For this retrospective study, 19 HCC patients undergoing 3D-CT, 4D-CT and multiple-parameter MR scans were included in this study. The gross tumor volume (GTV) was independently delineated twice by two observers based on contrast-enhanced computed tomography (CECT), maximum intensity projection (MIP), LAVA-Flex, T2W FRFSE and DWI-EPI images. We also delineated the peritumoral region, which was defined as 0 to 5 mm radius surrounding the GTV. 107 radiomic features were automatically extracted from CECT images using 3D-Slicer software. Quartile coefficient of dispersion (QCD) and intraclass correlation coefficient (ICC) were applied to assess the variability of each radiomic feature. QCD<10% and ICC≥0.75 were considered small variations and excellent reliability. Finally, the principal component analysis (PCA) was used to test the feasibility of dimensionality reduction.
For tumor tissues, the numbers of radiomic features with QCD<10% indicated no obvious inter-/intra-observer differences or discrepancies in 3D-CT, 4D-CT and multiple-parameter MR delineation. However, the number of radiomic features (mean 89) with ICC≥0.75 was the highest in the multiple-parameter MR group, followed by the 3DCT group (mean 77) and the MIP group (mean 73). The peritumor tissues also showed similar results. A total of 15 and 7 radiomic features presented excellent reproducibility and small variation in tumor and peritumoral tissues, respectively. Two robust features showed excellent reproducibility and small variation in tumor and peritumoral tissues. In addition, the values of the two features both represented statistically significant differences among tumor and peritumoral tissues (<0.05). The PCA results indicated that the first seven principal components could preserve at least 90% of the variance of the original set of features.
Delineation on multiple-parameter MR images could help to improve the reproducibility of the HCC CT radiomic features and weaken the inter-/intra-observer influence.
准确的病灶分割是提取放射组学特征的前提条件。它有助于减少特征变异性，从而提高放射组学研究的报告质量。在本研究中，我们旨在使用三维计算机断层扫描（3D-CT）图像、四维计算机断层扫描（4D-CT）图像和多参数磁共振成像（MR）图像，对肝细胞癌中观察者间/观察者内轮廓勾画的变异性进行放射组学特征重复性测试。
对于这项回顾性研究，本研究纳入了19例接受3D-CT、4D-CT和多参数MR扫描的肝癌患者。两位观察者基于对比增强计算机断层扫描（CECT）、最大强度投影（MIP）、LAVA-Flex、T2加权快速恢复快速自旋回波（T2W FRFSE）和扩散加权成像回波平面成像（DWI-EPI）图像，独立地对大体肿瘤体积（GTV）进行两次勾画。我们还勾画了瘤周区域，其定义为GTV周围半径0至5毫米的区域。使用3D-Slicer软件从CECT图像中自动提取107个放射组学特征。应用四分位数离散系数（QCD）和组内相关系数（ICC）来评估每个放射组学特征的变异性。QCD<10%且ICC≥0.75被认为变异小且可靠性高。最后，使用主成分分析（PCA）来测试降维的可行性。
对于肿瘤组织，QCD<10%的放射组学特征数量表明，在3D-CT、4D-CT和多参数MR勾画中，观察者间/观察者内没有明显差异或偏差。然而，ICC≥0.75的放射组学特征数量（平均89个）在多参数MR组中最高，其次是3DCT组（平均77个）和MIP组（平均73个）。瘤周组织也显示出类似的结果。分别有15个和7个放射组学特征在肿瘤组织和瘤周组织中表现出极好的重复性和小变异。两个稳健的特征在肿瘤组织和瘤周组织中表现出极好的重复性和小变异。此外，这两个特征的值在肿瘤组织和瘤周组织之间均表现出统计学显著差异（<0.05）。PCA结果表明，前七个主成分可以保留原始特征集至少90%的方差。
多参数MR图像上的轮廓勾画有助于提高肝癌CT放射组学特征的重复性，并减弱观察者间/观察者内的影响。
