应用管电压切换双能量光谱 CT 鉴别肺良恶性坏死性病变。
Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography.
机构信息
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, China.
Department of Radiology, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road Two, Yuzhong District, Chongqing, China.
出版信息
BMC Med Imaging. 2021 May 13;21(1):81. doi: 10.1186/s12880-021-00611-6.
BACKGROUND
Necrotic pulmonary lesions manifest as relatively low-density internally on contrast-enhanced computed tomography (CT). However, using CT to differentiate malignant and benign necrotic pulmonary lesions is challenging, as these lesions have similar peripheral enhancement. With the introduction of dual-energy spectral CT (DESCT), more quantitative parameters can be obtained and the ability to differentiate material compositions has been highly promoted. This study investigated the use of kVp-switching DESCT in differentiating malignant from benign necrotic lung lesions.
METHODS
From October 2016 to February 2019, 40 patients with necrotic lung cancer (NLC) and 31 with necrotic pulmonary mass-like inflammatory lesion (NPMIL) were enrolled and underwent DESCT. The clinical characteristics of patients, CT morphological features, and DESCT quantitative parameters of lesions were compared between the two groups. Binary logistic regression analysis was performed to identify the independent prognostic factors differentiating NPMIL from NLC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of single-parameter and multiparametric analyses.
RESULTS
Significant differences in age, C-reactive protein concentration, the slope of the spectral curve from 40 to 65 keV (K) of necrosis in non-contrast-enhanced scanning (NCS), arterial phase (AP) and venous phase (VP), effective atomic number of necrosis in NCS, and iodine concentration (IC) of the solid component in VP were observed between groups (all p < 0.05). The aforementioned parameters had area under the ROC curve (AUC) values of 0.747, 0.691, 0.841, 0.641, 0.660, 0.828, and 0.754, respectively, for distinguishing between NLC and NPMIL. Multiparametric analysis showed that age, K of necrosis in NCS, and IC of the solid component in VP were the most effective factors for differentiating NLC from NPMIL, with an AUC of 0.966 and percentage of correct class of 88.7%.
CONCLUSIONS
DESCT can differentiate malignant from benign necrotic lung lesions with a relatively high accuracy.
背景
坏死性肺病变在增强 CT 扫描中表现为相对低密度内部。然而，使用 CT 区分恶性和良性坏死性肺病变具有挑战性，因为这些病变具有相似的周边增强。随着双能光谱 CT（DESCT）的引入，可以获得更多的定量参数，并极大地提高了区分物质成分的能力。本研究探讨了使用 kVp 切换 DESCT 区分恶性和良性坏死性肺病变。
方法
2016 年 10 月至 2019 年 2 月，共纳入 40 例坏死性肺癌（NLC）患者和 31 例坏死性肺肿块样炎性病变（NPMIL）患者，并进行 DESCT 检查。比较两组患者的临床特征、CT 形态特征和病变的 DESCT 定量参数。采用二项逻辑回归分析鉴别 NPMIL 与 NLC 的独立预后因素。采用受试者工作特征（ROC）曲线评估单参数和多参数分析的诊断性能。
结果
两组患者的年龄、C 反应蛋白浓度、非增强扫描（NCS）、动脉期（AP）和静脉期（VP）坏死的光谱曲线斜率（K）、NCS 坏死的有效原子数和 VP 中实性成分的碘浓度（IC）差异有统计学意义（均 P<0.05）。上述参数鉴别 NLC 和 NPMIL 的 ROC 曲线下面积（AUC）值分别为 0.747、0.691、0.841、0.641、0.660、0.828 和 0.754。多参数分析显示，年龄、NCS 坏死 K 和 VP 中实性成分的 IC 是鉴别 NLC 和 NPMIL 的最有效因素，AUC 为 0.966，正确分类率为 88.7%。
结论
DESCT 能较准确地区分恶性和良性坏死性肺病变。
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