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肺内淋巴结伴周围炎症与胸膜下微浸润性肺腺癌的CT鉴别分析 被引量:6

CT differential analysis of pulmonary lymph nodes with peripheral inflammation and subpleural microinvasive lung adenocarcinoma
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摘要 目的:探讨肺内淋巴结(Intrapulmonary lymph nodes,IPLNs)伴周围炎症与胸膜下微浸润性肺腺癌(Minimally invasive adenocarcinoma,MIA)在多层螺旋CT上的鉴别点,提高两者的CT鉴别诊断能力。方法:回顾性分析本院经病理证实的20例肺内淋巴结伴周围炎症患者与31例胸膜下微浸润性肺腺癌患者的病例资料,分析两组患者临床特征及CT征象,并进行统计学分析。结果:两组的位置、实性成分占比、实性成分密度、尖角征、线状密度影具有统计学差异。结论:综合分析肺内淋巴结伴周围炎症在CT形态学表现如特殊的尖角征、线状密度影以及实性成分特征分析,有助于提高其与MIA鉴别,有效提示病灶性质。 Objective:To explore the differential diagnosis of intrapulmonary lymph nodes(IPLNs)with peripheral inflammation and subpleural microinvasive lung adenocarcinoma(minimally invasive adenocarcinoma,MIA)by multi-slice spiral CT,and to improve the ability of differential diagnosis.Methods:Retrospective analysis of 20 cases of IPLNs with peripheral inflammation and 31 cases of MIA was performed in our hospital.The clinical characteristics and CT signs of the two groups of patients were analyzed,and statistical analysis was performed on them.Results:There were statistical differences in the position,the proportion of solid components,the density of solid components,sharp angle sign and linear density shadow between the two groups.Conclusion:A comprehensive analysis of the CT morphological features of intrapulmonary lymph nodes with peripheral inflammation,such as special sharp angle sign,linear density shadow and solid component analysis,is helpful to improve the differentiation from MIA and effectively indicate the nature of the lesions.
作者 洪敏萍 王云华 顾吾立 黎良山 HONG Min-ping;WANG Yun-hua;GU Wu-li;LI Liang-shan(Department of Radiology,Traditional Chinese Medicine Hospital of Zhejiang Chinese Medical University,Jiaxing Zhejiang 314000,China;Department of Radiology,Haining People’s Hospital of Zhejiang Province,Jiaxing Zhejiang 314400,China)
出处 《中国临床医学影像杂志》 CAS 2020年第9期637-640,共4页 Journal of China Clinic Medical Imaging
基金 CT靶扫描在磨玻璃结节良恶性鉴别及肿瘤组织学类型预测中的应用研究(嘉兴市科学技术局),基金编号(2017AY33057)。
关键词 肺肿瘤 腺癌 淋巴结 体层摄影术 螺旋计算机 诊断 鉴别 Lung neoplasms Adenocarcinoma Lymph nodes Tomography,spiral computed Diagnosis,differential
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