李敏, 韩丽珠, 马菊香, 李琪, 王化, 叶兆祥. 双层探测器光谱CT对纯磨玻璃结节肺腺癌侵袭程度的鉴别价值[J]. 中国肿瘤临床, 2022, 49(14): 727-733. DOI: 10.12354/j.issn.1000-8179.2022.20211852
引用本文: 李敏, 韩丽珠, 马菊香, 李琪, 王化, 叶兆祥. 双层探测器光谱CT对纯磨玻璃结节肺腺癌侵袭程度的鉴别价值[J]. 中国肿瘤临床, 2022, 49(14): 727-733. DOI: 10.12354/j.issn.1000-8179.2022.20211852
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Min Li, Lizhu Han, Juxiang Ma, Qi Li, Hua Wang, Zhaoxiang Ye. Value of dual-layer spectral detector CT in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodule[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2022, 49(14): 727-733. DOI: 10.12354/j.issn.1000-8179.2022.20211852
Citation: Min Li, Lizhu Han, Juxiang Ma, Qi Li, Hua Wang, Zhaoxiang Ye. Value of dual-layer spectral detector CT in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodule[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2022, 49(14): 727-733. DOI: 10.12354/j.issn.1000-8179.2022.20211852
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双层探测器光谱CT对纯磨玻璃结节肺腺癌侵袭程度的鉴别价值

Value of dual-layer spectral detector CT in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodule

    摘要
  • 摘要:
      目的  探究双层探测器光谱CT对表现为肺纯磨玻璃结节（pure ground-glass nodule， pGGN）的微浸润腺癌（minimally invasive adenocarcinoma， MIA）和浸润性腺癌（invasive adenocarcinoma， IA）的鉴别价值。
      方法  回顾性分析天津医科大学肿瘤医院2019年8月至2021年6月术前行双层探测器光谱CT胸部增强扫描，CT上表现为pGGN，且术后病理证实为MIA和IA的101例（103枚pGGN）患者的临床和影像学资料，将pGGN分为MIA组（n=47）和IA组（n=56）。采用独立样本t检验、Mann-Whitney U检验和χ²检验比较两组间定量参数和CT征象的差异，使用多因素Logistic回归筛选出独立预测因子，并建立预测IA的模型。以ROC曲线评估预测模型和独立预测因子诊断效能，诊断效能比较采用DeLong检验。
      结果  与MIA组相比，IA组形状多表现为不规则（P=0.034），且更易出现胸膜牵拉征（P=0.005）、空气支气管征（P=0.001）及血管异常征（P<0.001）。IA组结节大小（P<0.001）、常规120 kVp图像的CT值（CT120 kVp， P=0.001）及40～200 keV单能量图像的CT值（CT40～200 keV， P≤0.003）均高于MIA组，而有效原子序数（effective atomic number， Eff-Z）低于MIA组（P=0.018）。Logistic回归分析表明结节大小（OR=1.435，95%CI:1.204～1.709，P<0.001）和CT200 keV（OR=1.011，95%CI:1.005～1.016，P<0.001）是预测IA的独立因子。ROC曲线分析显示预测模型的曲线下面积（area under the curve，AUC）最大，达到0.855，灵敏度为0.661，特异度为0.957，且AUC显著高于结节大小（P=0.046）和CT200 keV（P=0.002）。
      结论  双层探测器光谱CT对鉴别表现为pGGN的MIA和IA具有重要价值，经Logistic回归分析建立的预测模型有较好的准确性。

     

    Abstract:
      Objective  To assess the value of dual-layer spectral detector CT in distinguishing between lung minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN).
      Method  Clinical and imaging data of 101 patients (103 pGGN) with pGGN on preoperative chest enhanced dual-layer spectral detector CT and pathologically confirmed MIA and IA from August 2019 to June 2021 at Tianjin Medical University Cancer Institute & Hospital were retrospectively analyzed. pGGN cases were assigned into the MIA group (n=47) and IA group (n=56). Independent sample t-test, Mann-Whitney U test, and χ2 test were used to compare differences in CT features and quantitative parameters between the two groups. Multivariate Logistic regression was used to identify independent predictors of IA, and a predictive model for IA was constructed. The diagnostic performance of each independent predictors and the model was evaluated using ROC curve analysis, and DeLong test was used to compare the diagnostic efficacy of the model and independent predictors.
      Results  The IA group had higher frequencies of irregular shape (P=0.034), pleural retraction (P=0.005), air bronchogram (P=0.001), and vascular abnormalities (P<0.001) than the MIA group. Nodule size (P<0.001), CT value of traditional 120 kVp images (CT120 kVp, P=0.001), and 40–200 keV monochromatic images (CT40-200 keV, P≤0.003) were significantly higher in the IA group than in the MIA group, while the effective atomic number (Eff-Z) value was significantly lower in the IA group than in the MIA group (P=0.018). Logistic regression analysis showed that nodule size (OR=1.435, 95%CI:1.204-1.709, P<0.001) and CT200 keV (OR=1.011, 95%CI:1.005-1.016, P<0.001) were independent factors for predicting IA. ROC curve analysis showed that the area under the curve (AUC), sensitivity, and specificity of the predictive model were 0.855, 0.661, and 0.957, respectively, and AUC was significantly higher than nodule size (P=0.046) and CT200 keV (P=0.002).
      Conclusions  Dual-layer spectral detector CT is of great value in differentiating between MIA and IA manifesting as pGGNs and the predictive model established by Logistic regression analysis has good accuracy.

     

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