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基于双能 CT 构建预测以磨玻璃密度为主型肺腺癌侵袭性的联合预测模型

Predicting Pathological Invasiveness of Lung Adenocarcinoma Manifesting as GGO-Predominant Nodules: A Combined Prediction Model Generated From DECT.

机构信息

Department of Radiology, The Third Affiliated Hospital of Soochow University, No.185, Juqian Street, Changzhou 213003, Jiangsu, China.

Department of Radiology, The Third Affiliated Hospital of Soochow University, No.185, Juqian Street, Changzhou 213003, Jiangsu, China.

出版信息

Acad Radiol. 2021 Apr;28(4):509-516. doi: 10.1016/j.acra.2020.03.007. Epub 2020 Apr 14.

Abstract

RATIONALE AND OBJECTIVES

To evaluate qualitative and quantitative indicators generated from Dual-energy computed tomography (DECT) for preoperatively differentiating between invasive adenocarcinoma (IAC) and preinvasive or minimally invasive adenocarcinoma (MIA) lesions manifesting as ground-glass opacity-predominant (GGO-predominant) nodules.

MATERIALS AND METHODS

We retrospectively enrolled 143 cases of completely resected GGO-predominant lung adenocarcinoma with DECT examinations between December 2017 and July 2019. Qualitative and quantitative parameters of GGO-predominant nodules were compared after grouping nodules into IAC and preinvasive-MIA groups. A multivariate logistic regression models were used for analyzing these parameters. The diagnostic performance of different parameters was compared by receiver operating characteristic (ROC) curves and Z tests.

RESULTS

This study included 137 patients (58 years ± 11; male: female = 52:91) with 143 GGO-predominant nodules. The proportion of margins, internal dilated/distorted/cut-off bronchi, internal thickened/stiff/distorted vasculature, pleural indentation, and vascular convergence were higher in the IAC group than in the preinvasive-MIA group, as were the maximum diameter (D), the diameter of the solid component (D) and the enhanced monochromatic CT value at 40 keV-190 keV (CT) (p range: 0.001-0.019). Logistic regression analyses revealed that margin, D, and CT values were independent predictors of the IAC group. The area under the curve (AUC) for the combination of margin, D, and CT was 0.896 (90.2% sensitivity, 70.7% specificity, 84.6% accuracy), which was significantly higher than that for each two of them (all p < 0.05).

CONCLUSION

The combined prediction model generated from DECT allows for effective preoperative differentiation between IAC and preinvasive-MIA in GGO-predominant lung adenocarcinomas.

摘要

背景与目的

评估双能 CT(DECT)生成的定性和定量指标,以术前区分表现为磨玻璃密度为主(GGO 为主)结节的浸润性腺癌(IAC)和侵袭前或微侵袭性腺癌(MIA)病变。

材料与方法

我们回顾性纳入了 2017 年 12 月至 2019 年 7 月期间接受 DECT 检查的 143 例完全切除的 GGO 为主型肺腺癌患者。将结节分为 IAC 和侵袭前-MIA 组后,比较 GGO 为主结节的定性和定量参数。采用多变量逻辑回归模型分析这些参数。通过受试者工作特征(ROC)曲线和 Z 检验比较不同参数的诊断性能。

结果

本研究共纳入 137 例患者(58 岁±11;男:女=52:91),共 143 个 GGO 为主型结节。与侵袭前-MIA 组相比,IAC 组的边缘、内部扩张/扭曲/截断支气管、内部增厚/僵硬/扭曲血管、胸膜凹陷和血管汇聚的比例更高,最大直径(D)、实性成分直径(D)和 40keV-190keV 增强单能 CT 值(CT)更高(p 范围:0.001-0.019)。Logistic 回归分析显示,边缘、D 和 CT 值是 IAC 组的独立预测因子。边缘、D 和 CT 值联合的曲线下面积(AUC)为 0.896(90.2%的敏感性、70.7%的特异性、84.6%的准确性),明显高于两两联合(均 P<0.05)。

结论

DECT 生成的联合预测模型可有效术前区分 GGO 为主型肺腺癌中的 IAC 和侵袭前-MIA。

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