双层探测器光谱CT增强双期定量参数术前预测胃癌分化和E-钙黏素表达的价值  

The value of dual-phase contrast enhanced parameters of dual-layer detector spectral CT in preoperative prediction of gastric cancer differentiation and E-cadherin protein expression

作　　者：吴吟晨 佘德君[1] 王密[2] 熊美连[1] 马承乐 林金珠[1] 曹代荣[1] Wu Yinchen;She Dejun;Wang Mi;Xiong Meilian;Ma Chengle;Lin Jinzhu;Cao Dairong(Department of Radiology,the First Affiliated Hospital,Fujian Medical University,National Regional Medical Center,Binhai Campus of the First Affiliated Hospital,Fujian Medical University,Fuzhou 350005,China;Department of Pathology,the First Affiliated Hospital,Fujian Medical University,National Regional Medical Center,Binhai Campus of the First Affiliated Hospital,Fujian Medical University,Fuzhou 350005,China)

机构地区：[1]福建医科大学附属第一医院医学影像科、福建医科大学附属第一医院滨海院区国家区域医疗中心,福州350005 [2]福建医科大学附属第一医院病理科、福建医科大学附属第一医院滨海院区国家区域医疗中心,福州350005

出　　处：《中华放射学杂志》2024年第7期738-744,共7页Chinese Journal of Radiology

基　　金：福建省科技创新联合资金项目(2021Y9141)。

摘　　要：目的探讨双层探测器光谱CT动、静脉期定量参数对胃癌分化程度和E-钙黏素(ECAD)表达的预测价值。方法该研究为横断面研究, 回顾性分析2021年10月至2022年10月福建医科大学附属第一医院183例经手术病理证实为胃腺癌患者的术前光谱CT资料, 分别根据胃癌分化程度和ECAD表达水平分为中高分化组(82例)、低分化组(101例)和ECAD阴性组(80例)、ECAD阳性组(103例)。基于CT光谱图像重建动、静脉期40、50、60、70、80、90、100 keV虚拟单能量图像(VMI)、有效原子序数(Z_(eff))图和碘浓度(IC)图, 测量各VMI图像病灶CT值(CT_(keV))、Z_(eff)、IC, 计算标准化有效原子序数(NZ_(eff))和标准化碘浓度(NIC)。采用独立样本t检验或Mann-WhitneyU检验比较组间各定量参数的差异, 采用logistic回归筛选独立预测因素并建立联合参数模型。采用受试者操作特征曲线评估定量参数预测胃癌分化程度及ECAD表达的效能。结果中高分化组与低分化组间动、静脉期CT_(40~70 keV)、NZ_(eff)、NIC和静脉期Z_(eff)、IC差异有统计学意义(P<0.05), 动脉期CT40 keV(OR=1.03, 95%CI 1.02~1.05, P<0.001)、静脉期CT40 keV(OR=1.05, 95%CI 1.03~1.07, P<0.001)、静脉期Z_(eff)(OR=1.32, 95%CI 1.06~1.65, P=0.015)构建的联合参数模型区分中高分化与低分化胃癌的曲线下面积(AUC)为0.932(95%CI 0.897~0.967), 灵敏度为90.1%, 特异度为85.4%。ECAD阴性组与阳性组间动脉期CT40 keV、NZ_(eff)和静脉期CT_(40~70 keV)、Z_(eff)、NZ_(eff)、IC和NIC差异有统计学意义(P<0.05), 静脉期CT40 keV(OR=1.02, 95%CI 1.01~1.04, P<0.001)和静脉期Z_(eff)(OR=1.33, 95%CI 1.09~1.63, P=0.006)联合参数预测ECAD表达的AUC为0.800(95%CI 0.736~0.864), 灵敏度为95.0%, 特异度为60.2%。结论双层探测器光谱CT定量参数联合模型可用于术前预测胃癌分化和ECAD表达。Objective To investigate the predictive value of the quantitative parameters of dual-layer detector spectral CT in arterial and venous phases for the differentiation degree and the E-cadherin protein expression of gastric cancer.Methods This was a cross-sectional study.The preoperative data from the dual-layer detector spectral CT images among 183 patients with gastric adenocarcinoma confirmed by operation and pathology was retrospectively analyzed from October 2021 to October 2022 in the First Affiliated Hospital of Fujian Medical University.According to the differentiation degree and E-cadherin protein expression of gastric cancer,all patients were divided into the moderately well differentiated group(n=82)and the poorly differentiated group(n=101),as well as the E-cadherin-negative group(n=80)and the E-cadherin-positive group(n=103).The CT images in arterial and venous phases were used to reconstruct the virtual monoenergetic images(VMI)at 40,50,60,70,80,90,and 100 keV,effective atomic number(Z_(eff))images and iodine concentration(IC)images.The CT values(CT_(keV))from VMI,Z_(eff) and IC were measured,and the normalized Z_(eff)(NZ_(eff))and the normalized IC(NIC)were calculated.Independent-sample t test or Mann-Whitney U test were used to compare the differences in quantitative parameters between groups.The logistic regression analysis was used to screen for the independent predictors,after which a combined prediction model was constructed.The receiver operating characteristic curves were used to evaluate the predictive efficiency of the parameters for the differentiation degree and the E-cadherin protein expression of gastric cancer.Results There were statistically significant differences in CT_(40 keV) to CT_(70 keV),NZ_(eff) and NIC in dual-phase,as well as Z_(eff) and IC in the venous phase between the moderately well differentiated group and the poorly differentiated group(P<0.05).The combined prediction model was constructed by CT_(40 keV)(OR=1.03,95%CI 1.02-1.05,P<0.001)in arterial phase and CT_(40 keV)

关 键 词：胃肿瘤 腺癌 体层摄影术 X线计算机 诊断效能 

分 类 号：R735.2[医药卫生—肿瘤] R730.44[医药卫生—临床医学]