Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, No. 6 Jiefang St., Zhongshan District, Dalian Liaoning 116001, China.
Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou Jiangsu 225100, China.
Clin Radiol. 2022 Jan;77(1):e20-e26. doi: 10.1016/j.crad.2021.10.007. Epub 2021 Nov 10.
To explore the value of roundness measurement based on thin-section axial, coronal, and sagittal section computed tomography (CT) images for predicting pure ground-glass nodule (pGGN) invasiveness.
A total of 168 pGGNs in 155 patients (44 male, 111 females; mean age, 55.74 ± 10.57 years), and confirmed by surgery and histopathology, were analysed retrospectively and divided into pre-invasive (n=72) and invasive (n=96) groups. Photoshop (CS6) software was used to measure pGGN roundness based on conventional axial section, as well as coronal and sagittal sections generated by multiplanar reformation, from thin-section (1-mm-thick) CT lung images.
pGGN roundness values, measured in axial, coronal, and sagittal thin-section CT sections from the pre-invasive group were 0.8 ± 0.049, 0.816 ± 0.05, and 0.818 ± 0.043, respectively, while those in the invasive group were 0.745 ± 0.077, 0.684 ± 0.106, and 0.678 ± 0.106; differences between the two groups were significant (all p<0.001). Binary logistic regression analysis showed that roundness values based on coronal and sagittal sections (p<0.001) were better than those from axial sections (p>0.05) in predicting pGGN invasiveness, with odds ratio (OR) values of 14.858 and 23.315, respectively. ROC analysis showed that evaluation of roundness measured in sagittal sections was better at predicting pGGN invasiveness than when coronal sections were used (AUC 0.870 versus 0.832).
Roundness is useful for predicting pGGN invasiveness, with measurements from coronal and sagittal sections better than those from conventional axial sections, with sagittal section images having the best predictive value.
探讨基于薄层轴位、冠状位和矢状位 CT 图像的圆形度测量在预测纯磨玻璃结节（pGGN）侵袭性中的价值。
回顾性分析了 155 例（男 44 例，女 111 例；平均年龄 55.74±10.57 岁）经手术和组织病理学证实的 168 个 pGGN，将其分为非侵袭性（n=72）和侵袭性（n=96）两组。使用 Photoshop（CS6）软件，根据薄层（1mm 厚）CT 肺部图像，在传统的轴位以及多平面重建生成的冠状位和矢状位上，对 pGGN 的圆形度进行测量。
非侵袭性组 pGGN 在薄层 CT 轴位、冠状位和矢状位上的圆形度值分别为 0.8±0.049、0.816±0.05 和 0.818±0.043，而侵袭性组则分别为 0.745±0.077、0.684±0.106 和 0.678±0.106；两组之间差异均有统计学意义（均 P<0.001）。二项逻辑回归分析显示，冠状位和矢状位的圆形度值（P<0.001）比轴位的圆形度值（P>0.05）更能预测 pGGN 的侵袭性，其优势比（OR）值分别为 14.858 和 23.315。ROC 分析显示，在预测 pGGN 侵袭性方面，矢状位的圆形度评估优于冠状位（AUC 0.870 与 0.832）。
圆形度对预测 pGGN 的侵袭性有一定价值，冠状位和矢状位的测量值优于常规轴位，其中矢状位的预测价值最高。
