Department of Diagnostic Radiology, National Cancer Center, Cancer Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100021, China.
Department of Diagnostic Radiology, National Cancer Center, Cancer Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100021, China; PET-CT Center, National Cancer Center, Cancer Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100021, China.
Clin Radiol. 2019 Jan;74(1):59-66. doi: 10.1016/j.crad.2018.07.103. Epub 2018 Nov 27.
To analyse subsolid nodules (SSNs) detected during low-dose (LD) computed tomography (CT) screening and investigated whether CT texture analysis parameters can predict the malignancy and growth trends of GGNs.
In this retrospective study, 89 SSNs were detected in 86 LDCT screening participants, including 42 pure ground-glass nodules (GGNs) and 47 part-solid GGNs. In these participants, 28 SSNs were diagnosed as lung cancer at histopathology, and 61 SSNs from participants who underwent at least two LDCT imaging studies. All nodules were divided into three groups: cancer group, growth group, and non-growth group. The nodule size, volume, attenuation, volume doubling time (VDT), and texture parameters (mean value, uniformity, entropy and energy) were assessed, respectively.
The entropy of the cancer group was significantly higher than that of the growth and non-growth groups (pure GGNs: p=0.009, 0.001; part-solid GGNs: p=0.012, 0.004). The energy of the cancer group was significantly lower than that of the other groups (pure GGNs: p=0.043, 0.021; part-solid GGNs: p=0.001, 0.002). A good positive correlation was found between uniformity and VDT (p=0.022).
Different CT texture parameters show good predictive value for SSNs detected at LDCT screening: the entropy and energy differences between malignant pulmonary nodules and others could be a helpful quantitative index to predict the malignancy of SSNs. Uniformity could be used to predict the growth probability of pure GGNs at baseline to pay more attention to these nodules. Moreover, the follow-up and treatment plan could be more targeted.
分析低剂量（LD）计算机断层扫描（CT）筛查中检测到的亚实性结节（SSN），并探讨 CT 纹理分析参数是否可以预测磨玻璃结节（GGN）的恶性程度和生长趋势。
在这项回顾性研究中，86 名 LDCT 筛查参与者共检出 89 个 SSN，其中 42 个为纯磨玻璃结节（pGGN），47 个为部分实性 GGN。在这些参与者中，28 个 SSN 经组织病理学诊断为肺癌，61 个 SSN 来自至少接受了两次 LDCT 成像研究的参与者。所有结节均分为三组：癌症组、生长组和非生长组。分别评估结节大小、体积、衰减、体积倍增时间（VDT）和纹理参数（平均值、均匀性、熵和能量）。
癌症组的熵明显高于生长组和非生长组（纯 GGN：p=0.009，0.001；部分实性 GGN：p=0.012，0.004）。癌症组的能量明显低于其他组（纯 GGN：p=0.043，0.021；部分实性 GGN：p=0.001，0.002）。均匀性与 VDT 呈良好正相关（p=0.022）。
LDCT 筛查中检测到的 SSN 的不同 CT 纹理参数具有良好的预测价值：恶性肺结节与其他结节之间的熵和能量差异可能是预测 SSN 恶性程度的有用定量指标。均匀性可用于预测基线时纯 GGN 的生长概率，以便更加关注这些结节。此外，还可以更有针对性地制定随访和治疗计划。
