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作 者:赵娇 李建华[1] 费佳 李凤 黎佳维 伍建林[1] ZHAO Jiao;LI Jian-hua;FEI Jia(Department of Radiology,the Affiliated Zhongshan Hospital of Dalian University,Dalian 116001,China)
机构地区:[1]大连大学附属中山医院放射科,大连116001
出 处:《放射学实践》2018年第4期383-388,共6页Radiologic Practice
基 金:大连市科技局科技计划项目(2015E12SF120)
摘 要:目的:探讨肺磨玻璃结节(GGN)CT表现中有助于预判肺腺癌浸润性的征象,为临床术前评估和判定预后提供必要的影像学依据。方法:搜集176例经手术病理证实的GGN患者的临床及CT资料,其中非浸润腺癌组107例、浸润性腺癌组(IA)69例,比较两组GGN的CT表现,对有统计学差异的变量行二元logistic回归分析,绘制受试者工作特征(ROC)曲线,确定鉴别诊断的最佳临界值。结果:主要CT表现:(1)非浸润腺癌组多为纯磨玻璃样结节(89.7%),IA组多为混合磨玻璃样结节(69.6%);两组间分叶征、毛刺征、胸膜凹陷征及空气支气管征的出现率的差异有统计学意义(P<0.05)。(2)IA组GGN的平均直径为(1.61±0.59)cm、CT值为(-542.99±92.95)HU,均明显大于非浸润腺癌组[分别为(1.01±0.29)cm和(-627.95±97.40)HU],差异均有统计学意义(P<0.001);IA组GGN的相对CT值(结节与肺组织CT值的比值)低于非浸润腺癌组(分别为0.63±0.11和0.72±0.10),差异有统计学意义(P<0.001)。(3)二元logistic回归分析显示GGN的直径和空气支气管征是预测肺癌浸润性的重要因素。结论:肺磨玻璃结节的CT征象有助于术前预判肺腺癌的浸润性,从而可指导临床合理选择治疗或随诊方案。Objective:To explore CT features of ground glass opacity nodule(GGN)in predicting the invasion of pulmonary adenocarcinoma,and to provide essential imaging basis for preoperative evaluation and determining prognosis of GGN.Methods:A total of 176 cases with GGN lesions were divided into two groups according to histopathological types,69 cases of invasive adenocarcinoma(IA),107 cases of non-IA.The imaging features of these two groups were analyzed and compared.The variables that exhibited statistically significant differences were included in a binary logistic regression analysis.The optimal cutoff value for lesion size between non-IA and IA was calculated by using a receiver operating characteristic(ROC)curve.Results:①Most non-IA presented as pure-GGN(89.7%),IA presented largely as mixed-GGN(69.6%)on CT images.There were ststistically signi-ficant differences in lobulation,spiculation,pleural indentation and air bronchogram between the non-IA and IA(P<0.05).②IA had larger size[(1.61±0.59)cm]and higer CT attenuation[(-542.99±92.95)HU]than that of non-IA lesions[(1.01±0.29)cm in size;(-627.95±97.40)HU in CT attenuation;all P<0.001];the relative CT value of IA was lower than that of non-IA(0.63±0.11 vs 0.72±0.10,P<0.001).③The binary logistic regression analysis showed GGN lesion size and air bronchogram were significant factors of predicting the invasion of GGN.Conclusion:Accurate analysis of the CT image features of GGN could be useful for distinguish non-IA from IA,and may be helpful to determine strategies of follow-up or appropriate management for GGN.
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