DECT定量参数联合形态学特征预测肺实性结节浸润性非黏液腺癌的分化程度  

DECT Quantitative Parameters and Morphological Features to Predict Differentiation Degree of Invasive Non-Mucinous Adenocarcinoma in Solid Pulmonary Nodules

作　　者：何长久[1] 刘杰克 青浩渺[1] 李勇[1] 林礼波 解超莲 周鹏[1] 胡仕北[1] HE Changjiu;LIU Jieke;QING Haomiao(Department of Radiology,Sichuan Clinical Research Center for Cancer,Sichuan Cancer Hospital&Institute,Sichuan Cancer Center,University of Electronic Science and Technology of China,Chengdu,Sichuan Province 610041,P.R.China)

机构地区：[1]四川省肿瘤医院·研究所,四川省肿瘤临床医学研究中心,四川省癌症防治中心,电子科技大学附属肿瘤医院影像科,成都610041

出　　处：《临床放射学杂志》2025年第4期638-644,共7页Journal of Clinical Radiology

摘　　要：目的探讨双能量CT(DECT)定量参数联合形态学特征预测肺实性结节浸润性非黏液腺癌分化程度的价值。方法回顾性分析120例行双期胸部DECT增强扫描且经病理证实为浸润性非黏液腺癌患者的120个肺实性结节,包括中/高分化组(60例)和低分化组(60例)。分析肺结节的传统CT特征[直径、动静脉期虚拟平扫(VNC_A、VNC_V)的CT值、形状、边缘、分叶、毛刺、胸膜反应、空气支气管征、空泡征和血管集束征]和DECT定量特征[动静脉期电子云密度(Rho_A、Rho_V)、标准化碘含量(NIC_A、NIC_V)、有效原子序数(Zeff_A、Zeff_V)和能谱曲线斜率(λ_(HU)_A、λ_(HU)_V)]。应用多因素Logistic回归构建传统CT模型、DECT模型和联合CT模型,并比较模型之间的诊断效能。结果传统CT模型(特征:VNC_A的CT值和分叶)、DECT模型(特征:Rho_V、NIC_A、NIC_V和λ_(HU)_A)和联合CT模型(特征:NIC_A、NIC_V、λ_(HU)_A和分叶)的曲线下面积(AUC)分别为0.726(95%CI:0.637~0.803)、0.831(95%CI:0.752~0.894)和0.837(95%CI:0.758~0.898)。联合CT模型的AUC明显高于传统CT模型(Z=2.057,P<0.05),但与DECT模型相比差异无统计学意义(Z=0.288,P>0.05)。结论由NIC_A、NIC_V、λ_(HU)_A和分叶构建的联合CT模型有助于预测肺实性结节浸润性非黏液腺癌的分化程度。Objective To investigate the value of dual-energy CT(DECT)quantitative parameters and morphological features in predicting the differentiation degree of invasive non-mucinous adenocarcinoma in solid pulmonary nodules.Methods A total of 120 solid pulmonary nodules from 120 patients,who underwent dual-phase enhanced DECT of chest and were pathologically confirmed invasive non-mucinous adenocarcinoma,were retrospectively analyzed,including well/moderately differentiated group(n=60)and poorly differentiated group(n=60).The conventional CT features[including diameter,CT values of virtual non-contrast in arterial(VNC_(A))and venous phase(VNC_(V)),shape,margin,lobulation,spiculation,pleural reaction,air bronchogram sign,bubble sign,and vascular convergence sign]and DECT quantitative features[electron density(Rho_(A) and Rho_(V)),normalized iodine concentration(NIC_(A) and NIC_(V)),effective atomic number(Zeff_(A) and Zeff_(V)),and slope of energy spectrum curve(λ_(HU)_(A) andλ_(HU)_(V))in arterial and venous phase]of pulmonary nodules were analyzed.Traditional CT model,DECT model,and combined CT model were constructed using multivariate logistic regression.Diagnostic performance among the models was compared.Results The areas under the curve(AUCs)of the conventional CT model(features:CT values of VNC_(A) and lobulation),DECT model(features:Rho_(V),NIC_(A),NIC_(V),andλ_(HU)_(A)),and combined CT model(features:NIC_(A),NIC_(V),λ_(HU)_(A),and lobulation)were 0.726(95%CI:0.637-0.803),0.831(95%CI:0.752-0.894),and 0.837(95%CI:0.758-0.898),respectively.The AUC of the combined CT model was significantly higher than that of the traditional CT model(Z=2.057,P<0.05),but no significant difference in AUC was found between the combined CT model and the DECT model(Z=0.288,P>0.05).Conclusion The combined CT model including NIC_(A),NIC_(V),λ_(HU)_(A),and lobulation may predict the differentiation degree of invasive non-mucinous adenocarcinoma in solid pulmonary nodules.

关 键 词：双能量CT 肺实性结节 定量参数 形态学特征 分化程度 

分 类 号：R73[医药卫生—肿瘤]