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能谱CT诊断非小细胞肺癌纵隔淋巴结转移的应用价值 被引量:21

Comparative imaging study of mediastinal lymph node from pre-surgery dual energy CT versus post-surgeron verifications in non-small cell lung cancer patients
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摘要 目的:探讨能谱CT(dual energy CT,DECT)诊断非小细胞肺癌(non-small cell lung cancer,NSCLC)纵隔淋巴结转移的应用价值。方法:选择2018年4月至2019年10月在北京大学第三医院接受胸部DECT检查且经术后病理诊断证实的NSCLC患者病例资料进行回顾性分析,共收集到病例57例,两名放射科医师共同分析患者术前CT图像,将轴位图像上所有短径(short-axis diameter,S)≥5 mm的纵隔淋巴结纳入本研究。测量淋巴结形态学参数长径(long-axis diameter,L)、S、短径与长径比值(ratio of short-axis diameter to long-axis diameter,S/L)以及能谱参数动脉期及静脉期碘浓度(iodine concentration,IC)、标准化碘浓度(normalized iodine concentration,NIC)、能谱曲线斜率及有效原子序数。比较转移与非转移淋巴结形态学指标及其能谱参数的差异,将有统计学差异的参数纳入Logistic回归方程筛选出有诊断价值的参数,并生成诊断淋巴结转移的联合变量,对淋巴结S、静脉期NIC及联合变量进行受试者工作特征(receiver operating characteristic,ROC)曲线分析。结果:57例患者中,术后病理诊断证实转移淋巴结49枚,非转移淋巴结938枚。CT轴位上共检出S≥5 mm纵隔淋巴结163枚(转移淋巴结49枚,非转移淋巴结114枚)。转移淋巴结的S、L及S/L均显著大于非转移淋巴结(P<0.05),转移淋巴结的能谱参数均显著低于非转移性淋巴结(P<0.05)。S是诊断淋巴结转移的最佳单一形态学指标,ROC曲线下面积(area under curve,AUC)为0.752,阈值8.5 mm,灵敏度67.4%,特异度73.7%,准确率71.8%。静脉期NIC为最佳单一能谱参数,AUC为0.861,阈值0.53,灵敏度95.9%,特异度70.2%,准确率77.9%。多因素分析显示S、静脉期NIC是转移淋巴结的独立预测因子。联合S、静脉期NIC诊断淋巴结转移的AUC为0.895,灵敏度79.6%,特异度87.7%,准确率85.3%,明显高于S(P<0.001)、静脉期NIC(P=0.037)。结论:DECT定量参数鉴别NSCLC患者纵隔淋巴结转移的价值优于形态学参数,联合S和静脉期NIC可提高术前诊断淋巴结转移的准确率。 Objective:To validate the value of dual energy CT(DECT)in the differentiation of mediastinal metastatic lymph nodes from non-metastatic lymph nodes in non-small cell lung cancer(NSCLC).Methods:In the study,57 surgically confirmed NSCLC patients who underwent enhanced DECT scan within 2 weeks before operation were enrolled.Two radiologists analyzed the CT images before operation.All mediastinal lymph nodes with short diameter≥5 mm on axial images were included in this study.The morphological parameters[long-axis diameter(L),short-axis diameter(S)and S/L of lymph nodes]and the DECT parameters[iodine concentration(IC),normalized iodine concentration(NIC),slope of spectral hounsfield unit curve(λHU)and effective atomic number(Zeff)in arterial and venous phase]were measured.The differences of morphological parameters and DECT parameters between metastatic and non-metastatic lymph nodes were compared.The parameters with significant difference were analyzed by the Logistic regression model,then a new predictive variable was established.Receiver operator characteristic(ROC)analyses were performed for S,NIC in venous phase and the new predictive variable.Results:In 57 patients,49 metastatic lymph nodes and 938 non-metastatic lymph nodes were confirmed by surgical pathology.A total of 163 mediastinal lymph nodes(49 metastatic,114 non-metastatic)with S≥5 mm were detected on axial CT images.The S,L and S/L of metastatic lymph nodes were significantly higher than those of non-metastatic lymph nodes(P<0.05).The DECT parameters of metastatic lymph nodes were significantly lower than those of non-metastatic lymph nodes(P<0.05).The best single morphological parameter for differentiation between metastatic and nonmetastatic lymph nodes was S(AUC,0.752;threshold,8.5 mm;sensitivity,67.4%;specificity,73.7%;accuracy,71.8%).The best single DECT parameter for differentiation between metastatic and nonmetastatic lymph nodes was NIC in venous phase(AUC,0.861;threshold,0.53;sensitivity,95.9%;specificity,70.2%;accuracy,77.9%).Multivariate analysis showed that S and NIC were independent predictors of lymph node metastasis.The AUC of combined S and NIC in the venous phase was 0.895(sensitivity,79.6%;specificity,87.7%;accuracy,85.3%),which were significantly higher than that of S(P<0.001)and NIC(P=0.037).Conclusion:The ability of quantitative DECT parameters to distinguish mediastinal lymph node metastasis in NSCLC patients is better than that of morphological parameters.Combined S and NIC in venous phase can be used to improve preoperative diagnostic accuracy of metastatic lymph nodes.
作者 朱巧 任翠 张艳 李美娇 王晓华 ZHU Qiao;REN Cui;ZHANG Yan;LI Mei-jiao;WANG Xiao-hua(Department of Radiology,Peking University Third Hospital,Beijing 100191,China)
出处 《北京大学学报(医学版)》 CAS CSCD 北大核心 2020年第4期730-737,共8页 Journal of Peking University:Health Sciences
基金 国家自然科学基金(81871326)。
关键词 非小细胞肺癌 淋巴结转移 体层摄影术 X射线计算机 Non-small cell lung cancer Lymph node metastasis Tomography,X-ray computed
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