多模态超声联合血清学肿瘤标志物在肺周围型病变良恶性鉴别中的应用价值  

Value of multimodal ultrasound combined with serum tumor markers in differential diagnosis of benign and malignant peripheral pulmonary lesions

作　　者：王瑞 陈炜[2] 王金萍[1] 李保启[1] Rui Wang;Wei Chen;Jinping Wang;Baoqi Li(Department of Ultrasound,First Affiliated Hospital,Anhui University of Chinese Medicine,Hefei 230031,China;Department of Respiratory,First Affiliated Hospital,Anhui University of Chinese Medicine,Hefei 230031,China)

机构地区：[1]安徽中医药大学第一附属医院超声医学科,合肥230031 [2]安徽中医药大学第一附属医院呼吸内科,合肥230031

出　　处：《中华医学超声杂志(电子版)》2024年第11期1048-1056,共9页Chinese Journal of Medical Ultrasound(Electronic Edition)

基　　金：国家青年岐黄学者支持项目;国家自然科学基金青年基金(81704060);安徽中医药大学2020年度科学研究项目(2020yfyzc49)。

摘　　要：目的探讨由灰阶超声、彩色多普勒超声、超声造影构成的多模态超声联合血清学肿瘤标志物组合在肺周围型病变良恶性鉴别诊断中的应用价值。方法回顾性分析2022年1月至2023年12月于安徽中医药大学第一附属医院行肺周围型病变超声造影检查的65例患者。按照病理结果分为良性组及恶性组,所有患者均行多模态超声检查及血清学肿瘤标志物组合检测,对获取的病灶最大径线、病灶形态、血流形态、血流信号分级、内部回声、边界、支气管征、坏死、造影剂到达病灶的始增时间(AT)、造影剂到达病灶与周围肺组织的始增时间差(T_(AT))、造影剂增强模式、造影剂灌注模式以及患者的血清学肿瘤标志物检测结果进行单因素分析,将单因素分析中差异有统计学意义的各因素纳入Logistic回归分析,建立肺周围型病变的多因素联合预测模型,并绘制受试者操作特征(ROC)曲线检验其诊断肺周围型病变良恶性的效能。结果单因素结果显示灰阶超声(病灶形态)、彩色多普勒超声(血流形态)、超声造影(AT、T_(AT))以及血清学肿瘤标志物组合在肺周围型病变的良恶性鉴别方面具有统计学意义(P<0.05)。Logistic回归分析显示灰阶超声(病灶形态)、彩色多普勒超声(血流形态)、超声造影(T_(AT))、血清学肿瘤标志物组合与肺周围型病变的良恶性显著相关,回归方程式为:Logit(P)=-6.996+0.909X^(病灶形态)+1.521X^(血流形态)+2.927X^(T_(AT))+2.553X^(血清学肿瘤标志物组合)。绘制灰阶超声(病灶形态)、彩色多普勒超声(血流形态)、超声造影(T_(AT))、血清学肿瘤标志物组合、多模态超声联合以及多模态超声联合血清学肿瘤标志物预测模型的ROC曲线,其中多模态超声联合血清学肿瘤标志物预测模型曲线下面积最大(0.904)。结论灰阶超声(病灶形态)、彩色多普勒超声(血流形态)、超声造影(T_(AT))以及血清学肿�ObjectiveTo evaluate the value of multimodal ultrasound consisting of grayscale ultrasound, color Doppler flow imaging, and contrast-enhanced ultrasound (CEUS) combined with serological tumor markers in the differential diagnosis of benign and malignant peripheral pulmonary lesions.MethodsA retrospective analysis was performed on 65 patients who underwent contrast-enhanced ultrasound examination for peripheral pulmonary lesions at the First Affiliated Hospital, Anhui University of Chinese Medicine from January 2022 to December 2023. They were divided into either a benign group or a malignant group according to the pathological results. All patients underwent multimodal ultrasound examination and detection of serological tumor markers. Lesion maximum diameter, lesion morphology, blood flow morphology, blood flow signal grade, internal echo, boundary, bronchial sign,necrosis, contrast agent arrival time (AT), the difference of arrival time between the lesion and the surrounding lung tissue (T_(AT)), contrast agent enhancement mode, perfusion mode, and the combination of serum tumor markers were recorded for univariate analysis. All factors considered statistically significant in the univariate analysis were included in Logistic regression analysis. A multi-factor combined prediction model for peripheral pulmonary lesions was established and receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficiency of the model.ResultsUnivariate analysis showed that there were statistically significant differences in lesion morphology on gray-scale ultrasound, blood flow morphology on Doppler flow imaging, AT and T_(AT) on contrast-enhanced ultrasound, and the combination of serum tumor markers between benign and malignant peripheral pulmonary lesions (P<0.05). Logistic regression analysis showed that lesion morphology on gray-scale ultrasound, blood flow morphology on color Doppler flow imaging, T_(AT) on contrast-enhanced ultrasound, and the combination of serum tumor markers were s

关 键 词：超声造影 肺周围型病变 始增时间 血清学肿瘤标志物 

分 类 号：R73[医药卫生—肿瘤]