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Article(id=1206995209053885253, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1206995206415668023, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.2022.11.1133, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1643040000000, receivedDateStr=2022-01-25, revisedDate=null, revisedDateStr=null, acceptedDate=1655827200000, acceptedDateStr=2022-06-22, onlineDate=1765699682725, onlineDateStr=2025-12-14, pubDate=1669564800000, pubDateStr=2022-11-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765699682725, onlineIssueDateStr=2025-12-14, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765699682725, creator=13701087609, updateTime=1765699682725, updator=13701087609, issue=Issue{id=1206995206415668023, tenantId=1146029695717560320, journalId=1189873630562394117, year='2022', volume='47', issue='11', pageStart='1063', pageEnd='1167', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1765699682092, creator=13701087609, updateTime=1765700231511, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1206997510904693630, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1206995206415668023, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1206997510908887935, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1206995206415668023, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1133, endPage=1143, ext={EN=ArticleExt(id=1206995209381040973, articleId=1206995209053885253, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Clinical value of dual-layer spectral detector CT in distinguishing diagnosis of pulmonary primary malignant tumor, chronic inflammation and tuberculosis, columnId=1190310109000602400, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Clinical Research, runingTitle=null, highlight=null, articleAbstract=

Objective To explore the clinical value of dual-layer spectral detector CT (DLCT) in distinguishing diagnosis of pulmonary primary malignant tumor, chronic inflammation and tuberculosis by measuring and analyzing the parameters and conventional CT signs. Methods The clinical data of 345 patients with pulmonary lesions were collected from August 2020 to June 2021, who underwent DLCT chest enhanced scan and obtained pathological results in People's Hospital of Gansu Province, and then divided into three groups: pulmonary primary malignant tumor group (n=187), chronic inflammation group (n=101) and tuberculosis group (n=57). The conventional CT signs of the three groups were retrospectively analyzed and the DLCT parameters were measured. The logistic regression analysis was performed for parameters with statistically significant differences, and then conventional CT signs diagnostic model, DLCT parameter diagnostic model and combined diagnostic model were established. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy of each model. Delong test was used to compare the AUC of each models. Results In distinguishing the conventional CT signs of the three lesions, statistical differences existed in the following indicators: the distance of lesions to pleura (P=0.009), morphology (P<0.001), density (P=0.001), the boundary between lesions and lung (P=0.001), lobulation (P<0.001), liquefaction necrosis (P=0.003), vascular cluster sign(P<0.001), halo sign (P=0.003), satellite focus (P=0.045), pleural effusion (P=0.002), enlarged lymph nodes in the mediastinum(P<0.001), effective atomic number (Zeff), iodine concentration (IC), normalization iodine concentration (NIC), energy spectrum curve slope (λHU), and arterial enhancement fraction (AEF) (P<0.001) both in the arterial phase (AP) and venous phase (VP).In the differential diagnosis of pulmonary primary malignant tumor and chronic inflammation, the boundary between lesion and lung tissue (P=0.009), lobulation (P<0.001), liquefaction necrosis (P<0.001), halo sign (P=0.025), mediastinal lymphadenopathy(P<0.001), λHU-AP (P=0.037) and λHU-VP (P=0.029) are independent influencing factors. In the differential diagnosis of pulmonary primary malignant tumor and tuberculosis, lesion morphology (P=0.019), vascular cluster sign (P=0.009), satellite focus (P=0.006),pleural effusion (P=0.001), AEF (P=0.041), λHU-AP (P=0.038) and λHU-VP (P<0.001) are independent influencing factors. Pleural effusion (P=0.002), mediastinal lymphadenopathy (P<0.001), NIC-VP (P=0.001), Zeff-VP (P=0.043), λHU-AP (P=0.015) and λHU-VP (P=0.023) are independent influencing factors in the differential diagnosis of chronic inflammation and tuberculosis. To pathology results for the gold standard, the AUC of conventional CT signs diagnostic model for distinguishing pulmonary primary malignant tumor and chronic inflammation, pulmonary primary malignant tumor and tuberculosis, chronic inflammation and tuberculosis were 0.827, 0.770 and 0.753. The AUC of DLCT parameter values for distinguishing pulmonary primary malignant tumor, chronic inflammation and tuberculosis were 0.905 0.909 and 0.824. The AUC of the combined model for distinguishing pulmonary primary malignant tumor, chronic inflammation and tuberculosis were 0.929, 0.942 and 0.889. Conclusion DLCT parameters combined with conventional CT signs may improve the differential diagnosis efficiency of pulmonary primary malignant tumor, chronic inflammation and tuberculosis.

, correspAuthors=Gang Huang, authorNote=null, correspAuthorsNote=
*E-mail:
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目的 探讨双层探测器光谱CT(DLCT)参数和常规CT征象在肺原发恶性肿瘤、肺慢性炎症及肺结核鉴别诊断中的应用价值。方法 选取2020年8月－2021年6月在甘肃省人民医院行DLCT胸部增强扫描并获得病理结果的345例肺部疾病患者，按照病理结果分为肺原发恶性肿瘤组(n=187)、肺慢性炎症组(n=101)与肺结核组(n=57)。回顾性分析三组病变的常规CT征象并测量其DLCT参数，对差异有统计学意义的参数进行logistic回归分析，分别建立常规CT征象诊断模型、DLCT参数诊断模型及联合诊断模型，使用受试者工作特征曲线(ROC)分析各模型的诊断效能，采用Delong检验比较各模型的曲线下面积(AUC)。结果 三组病变的常规CT征象包括病灶距胸膜距离(P=0.009)、形态(P<0.001)、密度(P=0.001)、与肺组织分界(P=0.001)，以及有无分叶(P<0.001)、液化坏死(P=0.003)、血管集束征(P<0.001)、晕征(P=0.003)、卫星灶(P=0.045)、胸腔积液(P=0.002)、纵隔淋巴结大(P<0.001)，DLCT参数包括动脉期(AP)及静脉期(VP)有效原子序数(Zeff)、碘密度(IC)、标准化碘密度(NIC)、能谱曲线斜率(λHU)、动脉强化分数(AEF)(P<0.001)均存在明显差异。在肺原发恶性肿瘤和慢性炎症的鉴别诊断中，病灶与肺组织分界(P=0.009)、分叶(P<0.001)、液化坏死(P<0.001)、晕征(P=0.025)、纵隔淋巴结大(P<0.001)、λHU-AP(P=0.037)和λHU-VP(P=0.029)为独立影响因素；在原发恶性肿瘤和肺结核的鉴别诊断中，病灶形态(P=0.019)、血管集束征(P=0.009)、卫星灶(P=0.006)、胸腔积液(P=0.001)、AEF(P=0.041)、λHU-AP(P=0.038)及λHU-VP(P<0.001)为独立影响因素；在慢性炎症和肺结核的鉴别诊断中，胸腔积液(P=0.002)、纵隔淋巴结大(P<0.001)、NIC-VP(P=0.001)、Zeff-VP(P=0.043)、λHU-AP(P=0.015)及λHU-VP(P=0.023)为独立影响因素。以病理结果为金标准，鉴别肺原发恶性肿瘤与肺慢性炎症、肺原发恶性肿瘤与肺结核、肺慢性炎症与肺结核时，常规CT征象诊断模型的AUC分别为0.827、0.770及0.753，DLCT参数诊断模型的AUC分别为0.905、0.909及0.824，常规CT征象和DLCT参数联合诊断模型的AUC分别为0.929、0.942、0.889。结论 DLCT参数联合常规CT征象可提高肺原发恶性肿瘤、肺慢性炎症及肺结核的鉴别诊断效能。

, correspAuthors=黄刚, authorNote=null, correspAuthorsNote=
黄刚，E-mail：
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郑小霞，硕士研究生，主要从事肺部疾病影像诊断方面的研究

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郑小霞，硕士研究生，主要从事肺部疾病影像诊断方面的研究

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郑小霞，硕士研究生，主要从事肺部疾病影像诊断方面的研究

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Clin Radiol, 2018, 73(12): 1033-1040., articleTitle=CT spectral parameters and serum tumour markers to differentiate histological types of cancer histology, refAbstract=null)], funds=[Fund(id=1207064235402679230, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, awardId=21JR7RA605, language=EN, fundingSource=Natural Science Foundation of Gansu Province(21JR7RA605), fundOrder=null, country=null), Fund(id=1207064235503342531, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, awardId=21JR7RA605, language=CN, fundingSource=甘肃省自然科学基金(21JR7RA605), fundOrder=null, country=null), Fund(id=1207064235583034309, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, awardId=XM_HR_ICON_2021_05, language=EN, fundingSource=ICON Scientific Research Fund of China Red Cross Foundation(XM_HR_ICON_2021_05), fundOrder=null, country=null), Fund(id=1207064235717252042, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, awardId=XM_HR_ICON_2021_05, language=CN, fundingSource=中国红十字基金会“ICON科研基金”(XM_HR_ICON_2021_05), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1207064230163993284, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, xref=1, ext=[AuthorCompanyExt(id=1207064230172381893, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, companyId=1207064230163993284, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, Gansu 730000, China), AuthorCompanyExt(id=1207064230176576198, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, companyId=1207064230163993284, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1甘肃中医药大学第一临床医学院，甘肃兰州 730000)]), AuthorCompany(id=1207064230252073674, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, xref=2, ext=[AuthorCompanyExt(id=1207064230256267979, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, companyId=1207064230252073674, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2Department of Radiology, People’s Hospital of Gansu Province, Lanzhou, Gansu 730000, China), AuthorCompanyExt(id=1207064230264656589, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, companyId=1207064230252073674, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2甘肃省人民医院放射科，甘肃兰州 730000)]), AuthorCompany(id=1207064230327571156, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, xref=3, ext=[AuthorCompanyExt(id=1207064230335959766, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, companyId=1207064230327571156, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3Clinical Science, Philips Healthcare, Shanghai 200070, China), AuthorCompanyExt(id=1207064230344348375, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, companyId=1207064230327571156, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3飞利浦医疗临床科研部，上海 200070)])], figs=[ArticleFig(id=1207064233573962613, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=EN, label=Fig. 1, caption=Dual-layer spectral detector CT graphics and pathology of pulmonary primary malignant tumor, chronic inflammation, and tuberculosis, figureFileSmall=2GizTf/iM31zH8uRE1JKOg==, figureFileBig=4s3BURWi13/sDsdcFV64tg==, tableContent=null), ArticleFig(id=1207064233649460090, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=CN, label=图1, caption=肺原发恶性肿瘤、肺慢性炎症及肺结核的典型DLCT图像及病理结果示例

肺腺癌患者，女，63岁；肺慢性炎症患者，女，70岁；肺结核患者，男，73岁。A. 术前常规CT图像示左肺下叶占位；B. 术中穿刺图像；C. 感兴趣区(ROI)选取，在穿刺路径选取3个ROI，同层主动脉选取一个ROI进行测量；D. 有效原子序数(Zeff)图，3例患者的Zeff-AP分别为8.48、7.79、7.60；E. 碘浓度，3例患者的IC-AP分别为1.54 mg/ml、0.70 mg/ml、0.49 mg/ml；F. 术后病理结果(HE×400)；G. 动脉期能谱衰减曲线

, figureFileSmall=2GizTf/iM31zH8uRE1JKOg==, figureFileBig=4s3BURWi13/sDsdcFV64tg==, tableContent=null), ArticleFig(id=1207064233758511996, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=EN, label=Fig. 2, caption=ROC curves of conventional CT signs, dual-layer spectral detector CT parameters and the combined diagnosis of pulmonary primary malignant tumor, chronic inflammation and tuberculosis, figureFileSmall=y3g3a5UtS3kq+lzVJ4nAfA==, figureFileBig=a6RFd6q2aoU0wdMIR0bOdA==, tableContent=null), ArticleFig(id=1207064233817232258, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=CN, label=图2, caption=常规CT征象、DLCT参数及二者联合鉴别诊断肺原发恶性肿瘤、肺慢性炎症及肺结核的ROC曲线

A. 肺原发恶性肿瘤与肺慢性炎症；B. 肺原发恶性肿瘤与肺结核；C. 肺慢性炎症与肺结核

, figureFileSmall=y3g3a5UtS3kq+lzVJ4nAfA==, figureFileBig=a6RFd6q2aoU0wdMIR0bOdA==, tableContent=null), ArticleFig(id=1207064233901118343, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=EN, label=Tab. 1, caption=

Comparison of general data and conventional CT findings of pulmonary disease patients among three groups

, figureFileSmall=null, figureFileBig=null, tableContent=
项目肺原发恶性肿瘤组(n=187)肺慢性炎症组(n=101)肺结核组(n=57)χ2/HP
性别[例(%)]   0.1480.929
 男133(71.1)72(71.3)42(73.7)
 女54(28.9)29(28.7)15(26.3)
年龄[岁，M(Q1, Q3)]64.0(57.0, 70.0)55.0(48.0, 65.0)54.0(45.0, 66.0)31.5800.154
病灶位置[例(%)]   9.7370.284
 左肺上叶52(27.8)36(35.6)12(21.0)
 左肺下叶37(19.8)19(18.8)13(22.8)
 右肺上叶50(26.7)25(24.8)16(28.1)
 右肺中叶8(4.3)6(5.9)7(12.3)
 右肺下叶40(21.4)15(14.9)9(15.8)
距胸膜距离[例(%)]   9.4360.009
 <1 cm90(48.1)66(65.3)36(63.2)
 ≥1 cm97(51.9)35(34.7)21(36.8)
病灶最大径[mm, M(Q1, Q3)]40.7(29.4, 57.1)35.3(25.6, 53.4)28.9(19.9, 41.3)16.7810.085
病灶形态[例(%)]   23.932<0.001
 圆形/类圆形144(77.0)53(52.5)29(50.9)
 不规则43(23.0)48(47.5)28(49.1)
密度[例(%)]   13.1870.001
 实性140(74.9)57(56.4)32(56.1)
 亚实性47(25.1)44(43.6)25(43.9)
与肺组织分界[例(%)]   14.6580.001
 清楚125(66.8)44(43.6)33(57.9)
 模糊62(33.2)57(56.4)24(42.1)
分叶[例(%)]   19.904<0.001
 有101(54.0)27(26.7)24(42.1)
 无86(46.0)74(73.3)33(57.9)
棘状突起[例(%)]   2.0360.375
 有30(16.0)11(10.9)6(10.5)
 无157(84.0)90(89.1)51(89.5)
毛刺[例(%)]   6.5460.162
 无89(47.6)58(57.4)25(43.8)
 长毛刺23(12.3)16(15.8)7(12.3)
 短毛刺75(40.1)27(26.8)25(43.9)
钙化[例(%)]   2.7310.243
 有8(4.3)3(3.0)5(8.8)
 无179(95.7)98(97.0)52(91.2)
空泡征[例(%)]   4.4550.108
 有43(23.0)13(12.9)10(17.5)
 无144(77.0)88(87.1)47(82.5)
空洞[例(%)]   3.9300.140
 有13(7.0)13(12.9)8(14.0)
 无174(93.0)88(87.1)49(86.0)
细支气管充气征[例(%)]   0.6010.741
 有48(25.7)22(21.8)13(22.8)
 无139(74.3)79(78.2)44(77.2)
液化坏死[例(%)]   11.9060.003
 有39(20.9)40(39.6)14(24.6)
 无148(79.1)61(60.4)43(75.4)
胸膜增厚[例(%)]   0.3220.851
 有86(46.0)43(42.6)26(15.6)
 无101(54.0)58(57.4)31(54.4)
胸膜凹陷[例(%)]   5.0900.078
 有49(26.2)18(17.8)8(14.0)
 无138(73.8)83(82.2)49(86.0)
血管集束征[例(%)]   15.865<0.001
 有41(21.9)11(10.9)1(1.8)
 无146(78.1)90(89.1)56(98.2)
晕征[例(%)]   11.5210.003
 有10(5.3)18(17.8)6(10.5)
 无177(94.7)83(82.2)51(89.5)
卫星灶[例(%)]   6.2040.045
 有7(3.7)5(5.0)7(12.3)
 无180(96.3)96(95.0)50(87.7)
胸腔积液[例(%)]   12.1550.002
 有43(23.0)29(28.7)3(5.3)
 无144(77.0)72(71.3)54(94.7)
纵隔淋巴结大[例(%)]   27.144<0.001
 有126(67.4)36(35.6)34(59.6)
 无61(32.6)65(64.4)23(40.4)  
), ArticleFig(id=1207064233993393035, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=CN, label=表1, caption=

三组肺部疾病患者一般资料及常规CT征象比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目肺原发恶性肿瘤组(n=187)肺慢性炎症组(n=101)肺结核组(n=57)χ2/HP
性别[例(%)]   0.1480.929
 男133(71.1)72(71.3)42(73.7)
 女54(28.9)29(28.7)15(26.3)
年龄[岁，M(Q1, Q3)]64.0(57.0, 70.0)55.0(48.0, 65.0)54.0(45.0, 66.0)31.5800.154
病灶位置[例(%)]   9.7370.284
 左肺上叶52(27.8)36(35.6)12(21.0)
 左肺下叶37(19.8)19(18.8)13(22.8)
 右肺上叶50(26.7)25(24.8)16(28.1)
 右肺中叶8(4.3)6(5.9)7(12.3)
 右肺下叶40(21.4)15(14.9)9(15.8)
距胸膜距离[例(%)]   9.4360.009
 <1 cm90(48.1)66(65.3)36(63.2)
 ≥1 cm97(51.9)35(34.7)21(36.8)
病灶最大径[mm, M(Q1, Q3)]40.7(29.4, 57.1)35.3(25.6, 53.4)28.9(19.9, 41.3)16.7810.085
病灶形态[例(%)]   23.932<0.001
 圆形/类圆形144(77.0)53(52.5)29(50.9)
 不规则43(23.0)48(47.5)28(49.1)
密度[例(%)]   13.1870.001
 实性140(74.9)57(56.4)32(56.1)
 亚实性47(25.1)44(43.6)25(43.9)
与肺组织分界[例(%)]   14.6580.001
 清楚125(66.8)44(43.6)33(57.9)
 模糊62(33.2)57(56.4)24(42.1)
分叶[例(%)]   19.904<0.001
 有101(54.0)27(26.7)24(42.1)
 无86(46.0)74(73.3)33(57.9)
棘状突起[例(%)]   2.0360.375
 有30(16.0)11(10.9)6(10.5)
 无157(84.0)90(89.1)51(89.5)
毛刺[例(%)]   6.5460.162
 无89(47.6)58(57.4)25(43.8)
 长毛刺23(12.3)16(15.8)7(12.3)
 短毛刺75(40.1)27(26.8)25(43.9)
钙化[例(%)]   2.7310.243
 有8(4.3)3(3.0)5(8.8)
 无179(95.7)98(97.0)52(91.2)
空泡征[例(%)]   4.4550.108
 有43(23.0)13(12.9)10(17.5)
 无144(77.0)88(87.1)47(82.5)
空洞[例(%)]   3.9300.140
 有13(7.0)13(12.9)8(14.0)
 无174(93.0)88(87.1)49(86.0)
细支气管充气征[例(%)]   0.6010.741
 有48(25.7)22(21.8)13(22.8)
 无139(74.3)79(78.2)44(77.2)
液化坏死[例(%)]   11.9060.003
 有39(20.9)40(39.6)14(24.6)
 无148(79.1)61(60.4)43(75.4)
胸膜增厚[例(%)]   0.3220.851
 有86(46.0)43(42.6)26(15.6)
 无101(54.0)58(57.4)31(54.4)
胸膜凹陷[例(%)]   5.0900.078
 有49(26.2)18(17.8)8(14.0)
 无138(73.8)83(82.2)49(86.0)
血管集束征[例(%)]   15.865<0.001
 有41(21.9)11(10.9)1(1.8)
 无146(78.1)90(89.1)56(98.2)
晕征[例(%)]   11.5210.003
 有10(5.3)18(17.8)6(10.5)
 无177(94.7)83(82.2)51(89.5)
卫星灶[例(%)]   6.2040.045
 有7(3.7)5(5.0)7(12.3)
 无180(96.3)96(95.0)50(87.7)
胸腔积液[例(%)]   12.1550.002
 有43(23.0)29(28.7)3(5.3)
 无144(77.0)72(71.3)54(94.7)
纵隔淋巴结大[例(%)]   27.144<0.001
 有126(67.4)36(35.6)34(59.6)
 无61(32.6)65(64.4)23(40.4)  
), ArticleFig(id=1207064234094056337, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=EN, label=Tab. 2, caption=

Comparison of conventional CT signs between pulmonary primary malignant tumor, chronic inflammation and tuberculosis

, figureFileSmall=null, figureFileBig=null, tableContent=
常规CT征象肺原发恶性肿瘤vs.肺慢性炎症肺原发恶性肿瘤vs.肺结核肺慢性炎症vs.肺结核
χ2Pχ2Pχ2P
距胸膜距离7.8310.0053.9510.0470.0760.782
形态18.258<0.00114.455<0.0010.0370.847
密度10.3070.0017.3640.0070.0010.971
与肺组织分界14.659<0.0011.5330.2162.9950.084
分叶19.763<0.0012.4780.1153.9390.047
液化坏死11.5800.0010.3530.5533.6650.056
血管集束征5.3960.02012.472<0.0013.1300.077a
晕征11.6260.0011.9120.1671.5050.220
卫星灶0.0300.857a5.8870.0151.8430.175
胸腔积液1.1440.2858.9780.00312.405<0.001
纵隔淋巴结大26.840<0.0011.1560.2828.5090.004
), ArticleFig(id=1207064234198913941, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=CN, label=表2, caption=

肺原发恶性肿瘤、肺慢性炎症及肺结核常规CT征象两两比较结果

, figureFileSmall=null, figureFileBig=null, tableContent=
常规CT征象肺原发恶性肿瘤vs.肺慢性炎症肺原发恶性肿瘤vs.肺结核肺慢性炎症vs.肺结核
χ2Pχ2Pχ2P
距胸膜距离7.8310.0053.9510.0470.0760.782
形态18.258<0.00114.455<0.0010.0370.847
密度10.3070.0017.3640.0070.0010.971
与肺组织分界14.659<0.0011.5330.2162.9950.084
分叶19.763<0.0012.4780.1153.9390.047
液化坏死11.5800.0010.3530.5533.6650.056
血管集束征5.3960.02012.472<0.0013.1300.077a
晕征11.6260.0011.9120.1671.5050.220
卫星灶0.0300.857a5.8870.0151.8430.175
胸腔积液1.1440.2858.9780.00312.405<0.001
纵隔淋巴结大26.840<0.0011.1560.2828.5090.004
), ArticleFig(id=1207064234312160151, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=EN, label=Tab. 3, caption=

Comparison of dual-layer spectral detector CT parameters of pulmonary primary malignant tumor, chronic inflammation and tuberculosis [M(Q1, Q3)]

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DLCT参数肺原发恶性肿瘤组(n=187)肺慢性炎症组(n=101)肺结核组(n=57)HP
常规CT值(HU)
 AP58.97(15.03, 70.07)57.13(42.88, 70.22)54.37(16.09, 64.94)1.1950.550
 VP62.00(55.13, 74.53)62.05(48.99, 73.98)58.63(48.06, 71.45)4.0070.135
虚拟平扫CT值(HU)
 AP39.27(34.93, 43.43)35.42(31.34, 43.12)37.43(30.93, 44.01)4.4980.106
 VP39.07(34.63, 43.67)38.72(33.24, 43.62)37.00(31.80, 43.13)1.5430.462
IC(mg/ml)
 AP1.13(0.74, 1.45)0.80(0.46, 1.23)(2)0.56(0.34, 0.91)(2)(3)45.549<0.001
 VP1.10(0.85, 1.39)0.95(0.56, 1.34)(1)0.75(0.50, 1.02)(2)(3)29.805<0.001
NIC
 AP0.10(0.07, 0.13)0.07(0.04, 0.12)(1)0.05(0.26, 0.82)(2)(3)41.017<0.001
 VP0.31(0.25, 0.39)0.29(0.16, 0.37)(1)0.22(0.12, 0.28)(2)(3)31.235<0.001
Zeff
 AP8.35(8.11, 8.52)7.95(7.75, 8.25)(2)7.69(7.56, 7.87)(2)(4)137.757<0.001
 VP8.31(8.17, 8.47)8.07(7.81, 8.26)(2)7.72(7.57, 7.91)(2)(4)143.260<0.001
 NAEF0.31(0.24, 0.38)0.28(0.21, 0.37)0.32(0.19, 0.42)1.2250.542
λHU
 AP1.26(0.75, 1.71)1.00(0.59, 1.65)0.48(0.08, 0.83)(2)(4)52.674<0.001
 VP1.42(1.06, 1.92)1.20(0.71, 1.68)(1)0.62(0.34, 0.91)(2)(4)75.507<0.001
AEF1.03(0.79, 1.25)0.96(0.72, 1.20)0.82(0.65, 1.00)(2)(3)15.769<0.001
), ArticleFig(id=1207064234463155098, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=CN, label=表3, caption=

肺原发恶性肿瘤、肺慢性炎症及肺结核的DLCT参数比较[M(Q1, Q3)]

, figureFileSmall=null, figureFileBig=null, tableContent=
DLCT参数肺原发恶性肿瘤组(n=187)肺慢性炎症组(n=101)肺结核组(n=57)HP
常规CT值(HU)
 AP58.97(15.03, 70.07)57.13(42.88, 70.22)54.37(16.09, 64.94)1.1950.550
 VP62.00(55.13, 74.53)62.05(48.99, 73.98)58.63(48.06, 71.45)4.0070.135
虚拟平扫CT值(HU)
 AP39.27(34.93, 43.43)35.42(31.34, 43.12)37.43(30.93, 44.01)4.4980.106
 VP39.07(34.63, 43.67)38.72(33.24, 43.62)37.00(31.80, 43.13)1.5430.462
IC(mg/ml)
 AP1.13(0.74, 1.45)0.80(0.46, 1.23)(2)0.56(0.34, 0.91)(2)(3)45.549<0.001
 VP1.10(0.85, 1.39)0.95(0.56, 1.34)(1)0.75(0.50, 1.02)(2)(3)29.805<0.001
NIC
 AP0.10(0.07, 0.13)0.07(0.04, 0.12)(1)0.05(0.26, 0.82)(2)(3)41.017<0.001
 VP0.31(0.25, 0.39)0.29(0.16, 0.37)(1)0.22(0.12, 0.28)(2)(3)31.235<0.001
Zeff
 AP8.35(8.11, 8.52)7.95(7.75, 8.25)(2)7.69(7.56, 7.87)(2)(4)137.757<0.001
 VP8.31(8.17, 8.47)8.07(7.81, 8.26)(2)7.72(7.57, 7.91)(2)(4)143.260<0.001
 NAEF0.31(0.24, 0.38)0.28(0.21, 0.37)0.32(0.19, 0.42)1.2250.542
λHU
 AP1.26(0.75, 1.71)1.00(0.59, 1.65)0.48(0.08, 0.83)(2)(4)52.674<0.001
 VP1.42(1.06, 1.92)1.20(0.71, 1.68)(1)0.62(0.34, 0.91)(2)(4)75.507<0.001
AEF1.03(0.79, 1.25)0.96(0.72, 1.20)0.82(0.65, 1.00)(2)(3)15.769<0.001
), ArticleFig(id=1207064234547041184, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=EN, label=Tab. 4, caption=

Logistic regression analysis of dual-layer spectral detector CT parameter in pulmonary primary malignant tumor and chronic inflammation

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DLCT参数βPOR95%CI
IC-VP0.9210.1242.5120.778~8.108
NIC-VP0.3110.8181.3650.097~19.235
AEF0.7240.0622.0640.963~4.420
λHU-AP–0.5840.0370.5580.322~0.965
λHU-VP0.5530.0291.7381.057~2.858
), ArticleFig(id=1207064234643510178, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=CN, label=表4, caption=

肺原发恶性肿瘤与肺慢性炎症DLCT参数logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
DLCT参数βPOR95%CI
IC-VP0.9210.1242.5120.778~8.108
NIC-VP0.3110.8181.3650.097~19.235
AEF0.7240.0622.0640.963~4.420
λHU-AP–0.5840.0370.5580.322~0.965
λHU-VP0.5530.0291.7381.057~2.858
), ArticleFig(id=1207064234731590568, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=EN, label=Tab. 5, caption=

Logistic regression analysis of dual-layer spectral detector CT parameter in pulmonary primary malignant tumor and tuberculosis

, figureFileSmall=null, figureFileBig=null, tableContent=
光谱CT参数βPOR95%CI
IC-VP–0.2690.6540.7640.235~2.482
NIC-VP0.0100.9971.0100.006~161.439
AEF1.3210.0413.7481.056~13.299
λHU-AP1.2860.0383.6171.072~12.205
λHU-VP2.289<0.0019.8672.725~35.721
), ArticleFig(id=1207064234815476647, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=CN, label=表5, caption=

肺原发恶性肿瘤与肺结核DLCT参数logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
光谱CT参数βPOR95%CI
IC-VP–0.2690.6540.7640.235~2.482
NIC-VP0.0100.9971.0100.006~161.439
AEF1.3210.0413.7481.056~13.299
λHU-AP1.2860.0383.6171.072~12.205
λHU-VP2.289<0.0019.8672.725~35.721
), ArticleFig(id=1207064234911945642, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=EN, label=Tab. 6, caption=

Logistic regression analysis of dual-layer spectral detector CT parameter in chronic inflammation and tuberculosis

, figureFileSmall=null, figureFileBig=null, tableContent=
DLCT参数βPOR95%CI
NIC-VP–9.8330.0010.0000.000~0.015
AEF0.5930.1161.8100.864~3.792
Zeff-AP0.3870.6591.4720.264~8.212
Zeff-VP2.3170.04310.1401.074~95.760
λHU-AP1.7520.0155.7681.406~23.659
λHU-VP1.8800.0236.5561.293~33.228
), ArticleFig(id=1207064235004220335, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=CN, label=表6, caption=

肺慢性炎症与肺结核DLCT参数logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
DLCT参数βPOR95%CI
NIC-VP–9.8330.0010.0000.000~0.015
AEF0.5930.1161.8100.864~3.792
Zeff-AP0.3870.6591.4720.264~8.212
Zeff-VP2.3170.04310.1401.074~95.760
λHU-AP1.7520.0155.7681.406~23.659
λHU-VP1.8800.0236.5561.293~33.228
), ArticleFig(id=1207064235092300723, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=EN, label=Tab. 7, caption=

Delong test of AUC for three diagnostic models

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疾病类型常规CT征象诊断模型vs. DLCT参数诊断模型常规CT征象诊断模型vs.联合诊断模型DLCT参数诊断模型vs.联合诊断模型
ZPZPZP
肺原发恶性肿瘤与肺慢性炎症2.5950.0104.882<0.0011.7410.082
肺原发恶性肿瘤与肺结核3.535<0.0015.836<0.0012.3380.019
肺慢性炎症与肺结核1.3140.1894.040<0.0012.5520.011
), ArticleFig(id=1207064235205546937, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1206995209053885253, language=CN, label=表7, caption=

三种诊断模型AUC的Delong检验

, figureFileSmall=null, figureFileBig=null, tableContent=
疾病类型常规CT征象诊断模型vs. DLCT参数诊断模型常规CT征象诊断模型vs.联合诊断模型DLCT参数诊断模型vs.联合诊断模型
ZPZPZP
肺原发恶性肿瘤与肺慢性炎症2.5950.0104.882<0.0011.7410.082
肺原发恶性肿瘤与肺结核3.535<0.0015.836<0.0012.3380.019
肺慢性炎症与肺结核1.3140.1894.040<0.0012.5520.011
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双层探测器光谱CT在肺原发恶性肿瘤、肺慢性炎症及肺结核鉴别诊断中的应用价值
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郑小霞 1 , 马娅琼 2 , 熊晟原 2 , 陈杏彪 3 , 郑文霞 1 , 崔雅琼 2 , 黄刚 2, *
解放军医学杂志 | 临床研究 2022,47(11): 1133-1143
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解放军医学杂志 | 临床研究 2022, 47(11): 1133-1143
双层探测器光谱CT在肺原发恶性肿瘤、肺慢性炎症及肺结核鉴别诊断中的应用价值
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郑小霞1, 马娅琼2, 熊晟原2, 陈杏彪3, 郑文霞1, 崔雅琼2, 黄刚2, *
作者信息
  • 1甘肃中医药大学第一临床医学院，甘肃兰州 730000
  • 2甘肃省人民医院放射科，甘肃兰州 730000
  • 3飞利浦医疗临床科研部，上海 200070

    • 郑小霞，硕士研究生，主要从事肺部疾病影像诊断方面的研究

通讯作者:

黄刚，E-mail：
Clinical value of dual-layer spectral detector CT in distinguishing diagnosis of pulmonary primary malignant tumor, chronic inflammation and tuberculosis
Xiao-Xia Zheng1, Ya-Qiong Ma2, Sheng-Yuan Xiong2, Xing-Biao Chen3, Wen-Xia Zheng1, Ya-Qiong Cui2, Gang Huang2, *
Affiliations
  • 1The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, Gansu 730000, China
  • 2Department of Radiology, People’s Hospital of Gansu Province, Lanzhou, Gansu 730000, China
  • 3Clinical Science, Philips Healthcare, Shanghai 200070, China
出版时间: 2022-11-28 doi: 10.11855/j.issn.0577-7402.2022.11.1133
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摘要
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目的 探讨双层探测器光谱CT(DLCT)参数和常规CT征象在肺原发恶性肿瘤、肺慢性炎症及肺结核鉴别诊断中的应用价值。方法 选取2020年8月－2021年6月在甘肃省人民医院行DLCT胸部增强扫描并获得病理结果的345例肺部疾病患者，按照病理结果分为肺原发恶性肿瘤组(n=187)、肺慢性炎症组(n=101)与肺结核组(n=57)。回顾性分析三组病变的常规CT征象并测量其DLCT参数，对差异有统计学意义的参数进行logistic回归分析，分别建立常规CT征象诊断模型、DLCT参数诊断模型及联合诊断模型，使用受试者工作特征曲线(ROC)分析各模型的诊断效能，采用Delong检验比较各模型的曲线下面积(AUC)。结果 三组病变的常规CT征象包括病灶距胸膜距离(P=0.009)、形态(P<0.001)、密度(P=0.001)、与肺组织分界(P=0.001)，以及有无分叶(P<0.001)、液化坏死(P=0.003)、血管集束征(P<0.001)、晕征(P=0.003)、卫星灶(P=0.045)、胸腔积液(P=0.002)、纵隔淋巴结大(P<0.001)，DLCT参数包括动脉期(AP)及静脉期(VP)有效原子序数(Zeff)、碘密度(IC)、标准化碘密度(NIC)、能谱曲线斜率(λHU)、动脉强化分数(AEF)(P<0.001)均存在明显差异。在肺原发恶性肿瘤和慢性炎症的鉴别诊断中，病灶与肺组织分界(P=0.009)、分叶(P<0.001)、液化坏死(P<0.001)、晕征(P=0.025)、纵隔淋巴结大(P<0.001)、λHU-AP(P=0.037)和λHU-VP(P=0.029)为独立影响因素；在原发恶性肿瘤和肺结核的鉴别诊断中，病灶形态(P=0.019)、血管集束征(P=0.009)、卫星灶(P=0.006)、胸腔积液(P=0.001)、AEF(P=0.041)、λHU-AP(P=0.038)及λHU-VP(P<0.001)为独立影响因素；在慢性炎症和肺结核的鉴别诊断中，胸腔积液(P=0.002)、纵隔淋巴结大(P<0.001)、NIC-VP(P=0.001)、Zeff-VP(P=0.043)、λHU-AP(P=0.015)及λHU-VP(P=0.023)为独立影响因素。以病理结果为金标准，鉴别肺原发恶性肿瘤与肺慢性炎症、肺原发恶性肿瘤与肺结核、肺慢性炎症与肺结核时，常规CT征象诊断模型的AUC分别为0.827、0.770及0.753，DLCT参数诊断模型的AUC分别为0.905、0.909及0.824，常规CT征象和DLCT参数联合诊断模型的AUC分别为0.929、0.942、0.889。结论 DLCT参数联合常规CT征象可提高肺原发恶性肿瘤、肺慢性炎症及肺结核的鉴别诊断效能。

肺  /  诊断，鉴别  /  双层探测器光谱CT  /  体层摄影技术，X线计算机

Objective To explore the clinical value of dual-layer spectral detector CT (DLCT) in distinguishing diagnosis of pulmonary primary malignant tumor, chronic inflammation and tuberculosis by measuring and analyzing the parameters and conventional CT signs. Methods The clinical data of 345 patients with pulmonary lesions were collected from August 2020 to June 2021, who underwent DLCT chest enhanced scan and obtained pathological results in People's Hospital of Gansu Province, and then divided into three groups: pulmonary primary malignant tumor group (n=187), chronic inflammation group (n=101) and tuberculosis group (n=57). The conventional CT signs of the three groups were retrospectively analyzed and the DLCT parameters were measured. The logistic regression analysis was performed for parameters with statistically significant differences, and then conventional CT signs diagnostic model, DLCT parameter diagnostic model and combined diagnostic model were established. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy of each model. Delong test was used to compare the AUC of each models. Results In distinguishing the conventional CT signs of the three lesions, statistical differences existed in the following indicators: the distance of lesions to pleura (P=0.009), morphology (P<0.001), density (P=0.001), the boundary between lesions and lung (P=0.001), lobulation (P<0.001), liquefaction necrosis (P=0.003), vascular cluster sign(P<0.001), halo sign (P=0.003), satellite focus (P=0.045), pleural effusion (P=0.002), enlarged lymph nodes in the mediastinum(P<0.001), effective atomic number (Zeff), iodine concentration (IC), normalization iodine concentration (NIC), energy spectrum curve slope (λHU), and arterial enhancement fraction (AEF) (P<0.001) both in the arterial phase (AP) and venous phase (VP).In the differential diagnosis of pulmonary primary malignant tumor and chronic inflammation, the boundary between lesion and lung tissue (P=0.009), lobulation (P<0.001), liquefaction necrosis (P<0.001), halo sign (P=0.025), mediastinal lymphadenopathy(P<0.001), λHU-AP (P=0.037) and λHU-VP (P=0.029) are independent influencing factors. In the differential diagnosis of pulmonary primary malignant tumor and tuberculosis, lesion morphology (P=0.019), vascular cluster sign (P=0.009), satellite focus (P=0.006),pleural effusion (P=0.001), AEF (P=0.041), λHU-AP (P=0.038) and λHU-VP (P<0.001) are independent influencing factors. Pleural effusion (P=0.002), mediastinal lymphadenopathy (P<0.001), NIC-VP (P=0.001), Zeff-VP (P=0.043), λHU-AP (P=0.015) and λHU-VP (P=0.023) are independent influencing factors in the differential diagnosis of chronic inflammation and tuberculosis. To pathology results for the gold standard, the AUC of conventional CT signs diagnostic model for distinguishing pulmonary primary malignant tumor and chronic inflammation, pulmonary primary malignant tumor and tuberculosis, chronic inflammation and tuberculosis were 0.827, 0.770 and 0.753. The AUC of DLCT parameter values for distinguishing pulmonary primary malignant tumor, chronic inflammation and tuberculosis were 0.905 0.909 and 0.824. The AUC of the combined model for distinguishing pulmonary primary malignant tumor, chronic inflammation and tuberculosis were 0.929, 0.942 and 0.889. Conclusion DLCT parameters combined with conventional CT signs may improve the differential diagnosis efficiency of pulmonary primary malignant tumor, chronic inflammation and tuberculosis.

pulmonary  /  diagnosis, differentiation  /  dual-layer spectral detector CT  /  tomography, X-ray computer
郑小霞, 马娅琼, 熊晟原, 陈杏彪, 郑文霞, 崔雅琼, 黄刚. 双层探测器光谱CT在肺原发恶性肿瘤、肺慢性炎症及肺结核鉴别诊断中的应用价值. 解放军医学杂志, 2022 , 47 (11) : 1133 -1143 . DOI: 10.11855/j.issn.0577-7402.2022.11.1133
Xiao-Xia Zheng, Ya-Qiong Ma, Sheng-Yuan Xiong, Xing-Biao Chen, Wen-Xia Zheng, Ya-Qiong Cui, Gang Huang. Clinical value of dual-layer spectral detector CT in distinguishing diagnosis of pulmonary primary malignant tumor, chronic inflammation and tuberculosis[J]. Medical Journal of Chinese People’s Liberation Army, 2022 , 47 (11) : 1133 -1143 . DOI: 10.11855/j.issn.0577-7402.2022.11.1133
正文
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肺部局灶性病变中恶性者以肺癌为主，良性者以炎症及肺结核为主，其鉴别诊断一直是肺部影像诊断的难点[1]。传统的影像学检查对上述病变的诊断基于病变的形态学特点和增强CT特点，若形态学不能确定肺部病变的良恶性，则强烈依赖活检和随访[2-3]。但活检为有创检查，随访则需要较长时间和多次扫描。有研究表明，能谱CT扫描得到的能谱曲线斜率(λHU)、碘浓度(iodine concentration，IC)及标准化碘浓度(normalized iodine concentration，NIC)在肺癌、炎性病灶及肺结核的鉴别诊断中具有较高价值[4-5]。双层探测器光谱CT(dual-layer spectral detector CT，DLCT)是最新的采用双层探测器技术的能谱CT，可在时间和空间完全配准的情况下采集高、低能量数据并对其解析后生成全息光谱图像(spectral-based imaging，SBI)数据包，即时得到所需的能谱序列图像[6]。研究发现，DLCT在肺良恶性结节的诊断、肺癌与肺转移癌的鉴别以及肺癌淋巴结转移的评估中具有重要的临床价值[3,7-8]。本研究对肺部局灶性病变的常规CT征象及DLCT参数进行分析，探讨其在肺原发恶性肿瘤、肺慢性炎症及肺结核鉴别诊断中的价值。
1 资料与方法
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1.1 研究对象
回顾性分析2020年8月－2021年6月于甘肃省人民医院行DLCT胸部增强扫描并获得病理结果的345例肺部疾病患者的肺部局灶性病变的影像及临床资料。纳入标准：(1)病灶最大径≥1.0 cm；(2)经手术或穿刺活检证实为肺原发恶性肿瘤、肺慢性炎症(慢性炎症诊断标准为病理提示慢性炎症，治疗后随访复查无明显变化或吸收好转)或肺结核(肺结核病理诊断标准为镜下表现为慢性肉芽肿性炎伴或不伴干酪样坏死，且结核分枝杆菌DNA检测阳性)；(3)手术或穿刺活检前行DLCT胸部增强扫描，影像资料完整。排除标准：(1)急、慢性化脓性炎症及真菌感染；(2)CT扫描距手术或穿刺活检时间>1个月；(3)图像质量欠佳，不能进行影像学分析；(4)既往有放化疗及免疫治疗史。本研究经甘肃省人民医院伦理委员会审核通过(2021-286)。
1.2 检查方法
采用IQon Spectral CT机(荷兰Philips Healthcare公司)对所有患者进行检查，均扫描动脉期(arterial phase，AP)及静脉期(venous phase，VP)。患者取仰卧位，双手举过头顶，扫描范围从肺尖至肺底(胸廓入口至肋膈角)。扫描参数：管电压120 kVp，自动毫安秒(140~250 mAs)，X线管转速0.5 s/周，螺距0.953，重建层厚0.9 mm，层间距0.9 mm，图像重建矩阵512×512。增强扫描用高压注射器经右侧手背静脉注入碘克沙醇(320 mgI/ml，爱尔兰GE Healthcare公司)，剂量1 ml/kg体重，流速3.5 ml/s。采用对比剂智能追踪阈值触发技术采集动脉期图像，触发点设在气管分叉下2 cm降主动脉层面，触发阈值为150 HU，动脉期扫描结束30 s后行静脉期扫描。纵隔窗：窗宽350，窗位60；肺窗：窗宽1600，窗位–600。
1.3 图像分析
1.3.1 常规CT征象分析
由1名具有5年以上及1名具有10年以上胸部CT诊断经验的放射科医师对病灶的形态学进行评估，意见不一致时由另1名具有10年以上胸部CT诊断经验的医师评估。常规CT征象包括病灶的位置、距胸膜的距离、形态、密度、边缘，以及是否具有分叶、棘状突起、毛刺、钙化、空泡征、空洞、细支气管充气征、液化坏死、胸膜增厚、胸膜凹陷征、血管集束征、晕征、卫星灶、胸腔积液及纵隔淋巴结大等，并于病灶最大横断面测量最大径。
1.3.2 DLCT参数测量
将所有数据重建成SBI数据包，在后处理工作站(IntelliSpace Portal v9，Philips Healthcare，Best，The Netherlands)对图像进行数据测量及分析。在AP图像中于病灶最大层面及其上下两层(经手术切除患者)或病变穿刺活检部位穿刺针路径(经皮肺穿刺活检患者)选取并勾画3个圆形感兴趣区(region of interest，ROI)，并尽量避开血管、支气管、坏死及钙化部位。同时在同一层面主动脉或锁骨下动脉中心(当肿瘤位于肺尖部时)放置一个覆盖血管面积60%~70%的ROI。为使AP与VP图像ROI的位置、形状和大小一致，使用复制和粘贴功能，在VP图像上生成与AP一致的4个ROI。分别测量上述ROI的常规CT值、虚拟平扫(virtual non-contrasted，VNC) CT值、40 keV CT值、100 keV CT值、有效原子序数(effective atomic number，Zeff)及IC。通过上述数据计算：λHU=(CT值40 keV－CT值100 keV)/60；NIC=IClesion/ICaorta；动脉强化分数(arterial enhancement fraction，AEF)=ICAP/ICVP；标准化动脉强化分数(normalized arterial enhancement fraction，NAEF)=NICAP/NICVP。
1.4 鉴别诊断模型的建立
根据病理结果将符合纳入标准且不符合排除标准的患者分为肺原发恶性肿瘤组(n=187)、肺慢性炎症组(n=101)及肺结核组(n=57)。收集所有患者的性别、年龄，分析其常规CT征象并测量DLCT参数。对三组患者的常规CT征象和DLCT参数进行比较，将具有统计学差异的CT征象及DLCT参数纳入logistic回归分析，分别建立常规CT征象诊断模型、DLCT参数诊断模型及联合诊断模型。对肺原发恶性肿瘤、肺慢性炎症及肺结核进行鉴别诊断，并采用受试者工作特征(receiver operating characteristic，ROC)曲线分析各模型的诊断效能，采用Delong检验比较曲线下面积(area under curve，AUC)的差别。
1.5 统计学处理
采用SPSS 25.0和MedCalc 20.014软件进行统计分析。采用Kolmogorov-Smirnov检验判断计量资料是否符合正态分布，符合正态分布者以$\bar{x}±s$表示，不符合正态分布者以M(Q1，Q3)表示，多组间比较采用Kruskal-Wall检验，进一步两两比较采用Bonferrnoi校正。定性资料以例(%)表示，组间比较采用χ2检验。P<0.05为差异有统计学意义。
2 结果
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2.1 一般资料
共纳入肺部疾病患者345例，男247例，女98例，年龄21~84岁。333例(96.5%)行经皮肺穿刺活检确诊(306例通过单纯经皮肺穿刺活检确诊，22例经穿刺+手术病理确诊，5例经穿刺活检+支气管镜活检确诊)，12例(3.5%)经术后病理确诊。其中肺原发恶性肿瘤187例[腺癌88例，鳞状细胞癌51例，小细胞肺癌34例，其他恶性肿瘤14例(肺肉瘤样癌7例，神经内分泌肿瘤6例，腺鳞癌1例)；设为肺原发恶性肿瘤组]，肺慢性炎症101例(细菌感染5例，病毒感染5例，混合感染3例，其余病例均无明确病原学结果；设为肺慢性炎症组)，肺结核57例(设为肺结核组)。
2.2 肺原发恶性肿瘤、肺慢性炎症及肺结核患者常规CT征象比较
三组患者在病变距胸膜距离(P=0.009)、病灶形态(P<0.001)、密度(P=0.001)、与肺组织分界(P=0.001)，以及有无分叶(P<0.001)、液化坏死(P=0.003)、血管集束征(P<0.001)、晕征(P=0.003)、卫星灶(P=0.045)、胸腔积液(P=0.002)、纵隔淋巴结大(P<0.001)等方面差异有统计学意义；三组患者的性别、年龄、病灶位置、病灶最大径，以及有无棘状突起、毛刺、钙化、空泡征、空洞、细支气管充气征、胸膜增厚、胸膜凹陷等方面差异均无统计学意义(P>0.05，表1)。
对表1中三组间有统计学差异的常规CT征象进行两两比较，结果显示，肺原发恶性肿瘤与肺慢性炎症在卫星灶(P=0.857)及胸腔积液(P=0.285)方面差异无统计学意义；肺原发恶性肿瘤与肺结核在病灶与肺组织分界(P=0.216)及有无分叶(P=0.115)、液化坏死(P=0.553)、晕征(P=0.167)和纵隔淋巴结大(P=0.282)方面差异无统计学意义；肺慢性炎症与肺结核在病灶距胸膜距离(P=0.782)、形态(P=0.847)、密度(P=0.971)、与肺组织分界(P=0.084)及有无液化坏死(P=0.056)、血管集束征(P=0.077)、晕征(P=0.220)、卫星灶(P=0.175)方面差异无统计学意义(表2)。
2.3 常规CT征象logistic回归分析
选取表2中差异有统计学意义的常规CT征象进行logistic回归分析。自变量赋值如下：距胸膜距离(1=≥1 cm，0=<1 cm)、形态(1=不规则，0=圆形/类圆形)、密度(1=亚实性，0=实性)、与肺组织分界(1=模糊，0=清楚)，分叶、液化坏死、血管集束征、晕征、卫星灶、胸腔积液、纵隔淋巴结大(1=有，0=无)。
2.3.1 肺原发恶性肿瘤与肺慢性炎症的鉴别诊断
以病理结果作为因变量(1=肺原发恶性肿瘤，0=肺慢性炎症)，将病灶距胸膜距离、形态、密度、与肺组织分界、分叶、液化坏死、血管集束征、晕征、纵隔淋巴结大作为自变量纳入二元logistic回归分析，结果显示，病灶与肺组织分界(P=0.009)、分叶(P<0.001)、液化坏死(P<0.001)、晕征(P=0.025)及纵隔淋巴结大(P<0.001)为肺原发恶性肿瘤与肺慢性炎症鉴别诊断的独立影响因素；相较肺慢性炎症，肺原发恶性肿瘤与肺组织分界清楚，更易出现分叶和纵隔淋巴结大，而液化坏死和晕征相对少见。
2.3.2 肺原发恶性肿瘤与肺结核的鉴别诊断
以病理结果作为因变量(1=肺原发恶性肿瘤，0=肺结核)，将病灶距胸膜距离、形态、密度、血管集束征、卫星灶、胸腔积液、纵隔淋巴结大作为自变量纳入二元logistic回归分析，结果显示，病灶形态(P=0.019)、血管集束征(P=0.009)、卫星灶(P=0.006)和胸腔积液(P=0.001)是肺原发恶性肿瘤与肺结核鉴别诊断的独立影响因素；相较肺结核，肺原发恶性肿瘤中血管集束征和胸腔积液多见，而肺结核病灶的形态更不规则，且易出现卫星灶。
2.3.3 肺慢性炎症与肺结核的鉴别诊断
以病理结果作为因变量(1=肺慢性炎症，0=肺结核)，将分叶、胸腔积液和纵隔淋巴结大作为自变量纳入二元logistic回归分析，结果显示，胸腔积液(P=0.002)和纵隔淋巴结大(P<0.001)是肺慢性炎症与肺结核鉴别诊断的独立影响因素；肺慢性炎症更易合并胸腔积液，而肺结核则更易出现纵隔淋巴结大。
2.4 肺原发恶性肿瘤、肺慢性炎症及肺结核患者DLCT参数比较
三组病变AP及VP的IC、NIC、Zeff、λHU、AEF差异均有统计学意义(P<0.001)，而AP及VP的常规CT值(PAP=0.550，PVP=0.135)、虚拟平扫CT值(PAP=0.106，PVP=0.462)以及NAEF(P=0.542)差异无统计学意义。上述有统计学差异的参数肺原发恶性肿瘤组>肺慢性炎症组>结核组，肺原发恶性肿瘤组与肺慢性炎症组的AEF(P=0.385)和λHU-AP(P=0.424)差异无统计学意义(表3、图1)。
2.5 DLCT参数logistic回归分析
对表3中三组间存在统计学差异的DLCT参数进行共线性诊断，删除方差膨胀因子(variance inflation factor，VIF)≥10的自变量。
2.5.1 肺原发恶性肿瘤与肺慢性炎症的鉴别诊断
经过共线性诊断，将IC-VP、NIC-VP、AEF、λHU-AP、λHU-VP作为自变量纳入二元logistic回归分析，结果显示，λHU-AP(P=0.037)和λHU-VP(P=0.029)是肺原发恶性肿瘤与肺慢性炎症鉴别诊断的独立影响因素(表4)。
2.5.2 肺原发恶性肿瘤与肺结核的鉴别诊断
经过共线性诊断，将IC-VP、NIC-VP、AEF、λHU-AP、λHU-VP作为自变量纳入二元logistic回归分析，结果显示，AEF(P=0.041)、λHU-AP(P=0.038)、λHU-VP(P<0.001)是肺原发恶性肿瘤与肺结核鉴别诊断的独立影响因素(表5)。
2.5.3 肺慢性炎症与肺结核的鉴别诊断
经过共线性诊断，将NIC-VP、AEF、Zeff-AP、Zeff-VP、λHU-AP、λHU-VP作为自变量纳入二元logistic回归分析，结果显示，NIC-VP(P=0.001)、Zeff-VP(P=0.043)、λHU-AP(P=0.015)、λHU-VP(P=0.023)是肺慢性炎症与肺结核鉴别诊断的独立影响因素(表6)。
2.6 常规CT征象、DLCT参数及联合诊断模型在肺原发恶性肿瘤、肺慢性炎症及肺结核鉴别诊断中的效能
ROC曲线分析结果显示，鉴别肺原发恶性肿瘤与肺慢性炎症、肺原发恶性肿瘤与肺结核、肺慢性炎症与肺结核时，常规CT征象诊断模型的AUC分别为0.827、0.770及0.753，DLCT参数诊断模型的AUC分别为0.905、0.909及0.824，常规CT征象和DLCT参数联合诊断模型的AUC分别为0.929、0.942、0.889(图2)。
Delong检验结果显示，在肺原发恶性肿瘤与肺慢性炎症的鉴别诊断中，常规CT征象诊断模型与DLCT参数诊断模型、联合诊断模型的AUC差异有统计学意义(P<0.05)，但联合诊断模型与DLCT参数诊断模型的AUC无明显差异(P=0.082)；在肺原发恶性肿瘤与肺结核的鉴别诊断中，三种诊断模型的AUC两两比较差异均有统计学意义(P<0.05)；在肺慢性炎症与肺结核的鉴别诊断中，联合诊断模型与DLCT征象诊断模型、DLCT参数诊断模型的AUC差异有统计学意义(P<0.05)，但常规CT征象与DLCT参数诊断模型的AUC无明显差异(P=0.189，表7)。
3 讨论
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胸部CT是肺部疾病的传统影像学诊断方法[9]。本研究常规CT观察到的病灶形态、密度、边缘、距胸膜的距离，以及有无血管集束征、晕征、分叶、卫星灶及胸腔积液在三组病变中存在统计学差异，通过上述多个变量建立logistic回归模型，鉴别肺原发恶性肿瘤与肺慢性炎症、肺原发恶性肿瘤与肺结核、肺慢性炎症与肺结核时，常规CT征象诊断模型的AUC分别为0.827、0.770及0.753，具有较高的诊断价值。但常规CT征象的评估依赖于放射医师的诊断水平。
DLCT基于双层探测器进行能谱分离，真正实现了同时、同源、同步和同向的能谱CT扫描[10]。DLCT为放射科医师提供了除CT值以外的更多参数选择以用于疾病的诊断鉴别。本研究结果显示，DLCT参数诊断模型在鉴别肺原发恶性肿瘤与肺慢性炎症、肺原发恶性肿瘤与肺结核、肺慢性炎症与肺结核时AUC分别为0.905、0.909及0.824，较常规CT征象诊断模型效能高。本研究中96.5%的患者通过经皮肺穿刺活检确诊，以穿刺路径选取ROI，组织病理切片和影像切片的匹配更加精确，结果显示，碘相关参数在肺原发恶性肿瘤组最高，其次为肺慢性炎症组，肺结核组最低，这可能与病灶的血管生成有关[4,7,11-12]。据此可鉴别形态学表现相似的肺原发恶性肿瘤、肺慢性炎症及肺结核。Wen等[7]研究发现，碘相关参数在恶性结节组高于良性结节组、非小细胞肺癌组高于小细胞肺癌组，而Zhang等[13]研究发现，碘相关参数在恶性肿瘤组高于良性肿瘤组，均与本研究结果一致。碘是造影剂的主要成分，基于碘基材料的图像能准确、客观地反映病变的IC[14]，而碘定量也可作为器官灌注的替代指标[15]。肺癌血供丰富，同时接受肺动脉和支气管动脉供血，以支气管动脉供血为主[16]，肿瘤新生血管较多，微血管密度加大，且肿瘤新生血管发育不成熟，无基底膜，通透性增加，造影剂容易渗漏到间隙，因此注射造影剂后可获得较长时间的强化[4,11]。肺慢性炎症为慢性炎细胞浸润及成纤维细胞增生，伴有不同程度的纤维化及肉芽组织形成。肺结核中心为干酪性坏死，周围环绕纤维组织增生，缺乏血供，无强化或周围环形强化[4-5]。组织内IC不仅与病变的血液供应密切相关，而且受到各种因素(如造影剂剂量、注射速率及个体差异)的影响[7,11,17-19]。因此，本研究采用了以主动脉IC为标准的NIC，更真实地反映了病变的血供情况。此外，有研究表明良性病变的NIC高于恶性病变[4,20-22]，与本研究结果不一致，分析原因可能与样本的选取、造影剂的使用、设备和扫描方案不同有关。本研究中增强扫描采用智能追踪阈值触发技术，最大限度地减少了个体差异的影响。Lin等[4]的研究纳入的炎症均为急性炎症，急性炎症血管增多且扩张，病变血供丰富，测得的数值可能更高，而本研究纳入的肺慢性炎症大多已机化形成肿块，在形态学上难以与肺肿瘤鉴别，需行活检确诊。
本研究logistic回归分析结果显示，λHU-AP及λHU-VP在肺原发恶性肿瘤与肺慢性炎症及肺结核的鉴别诊断中均为独立影响因素。能谱衰减曲线反映病变CT值随keV增加而发生的动态变化，用λHU量化表示，可用于评价不同组织的衰减特征[23]。由于结构和化学成分不同，每种物质都有特有的能谱曲线。肺原发恶性肿瘤、肺慢性炎症及肺结核分属不同的病理类型，组织细胞结构差异较大，能谱衰减曲线及λHU不同。能谱曲线在40~100 keV走行陡峭，而在100 keV以上走行平直，可能因为能级越大，X线穿透能力越强，组织吸收X线量减少，组织间差异也减小。AEF是鉴别诊断肺原发恶性肿瘤与肺结核的独立影响因素。AEF通过IC的变化反映病变组织的强化方式，肺原发恶性肿瘤血供丰富，增强后可迅速达到峰值，之后缓慢回落，而肺结核因为发生干酪样坏死而强化不明显。Zeff从原子序数推导而来，是不同物质的另一个定量指标，描述了每个像素的物质组成，提供了更多组织信息，可用于识别物质的成分，特别是密度相近、CT衰减值相同的物质[5,24]。Zeff亦被认为是反映肿瘤灌注的替代指标，与IC联合应用可间接提供病灶内造影剂聚集的信息[3,12]。Zeff-VP是鉴别诊断肺慢性炎症与肺结核的独立影响因素。NAEF在肺原发恶性肿瘤、肺结核和肺慢性炎症中无明显差异，与Wen等[7]的研究结果一致，提示不同期相强化幅度的变化不足以鉴别这三种疾病，也可能与样本量较少有关。
本研究存在以下不足：(1)肺结核组样本量较少，结果可能存在偏倚，且结核未能区分有无活动性；(2)未对肺原发恶性肿瘤进行病理亚型的区分，不同病理类型的肺原发恶性肿瘤光谱参数可能存在差异。后续需进行大样本量研究进一步探索。
综上所述，DLCT参数联合常规CT征象可明显提高肺原发恶性肿瘤、肺慢性炎症及肺结核的鉴别诊断效能，具有较高的临床应用价值，在常规形态学诊断困难时依据DLCT参数尤其是λHU可提高术前诊断能力，降低因术前诊断困难而对良性病变进行不必要手术和活检的概率。
基金
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  • 甘肃省自然科学基金(21JR7RA605)
  • 中国红十字基金会“ICON科研基金”(XM_HR_ICON_2021_05)
文献
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2022年第47卷第11期
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doi: 10.11855/j.issn.0577-7402.2022.11.1133
  • 接收时间：2022-01-25
  • 首发时间：2025-12-14
  • 出版时间：2022-11-28
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  • 收稿日期：2022-01-25
  • 录用日期：2022-06-22
基金
Natural Science Foundation of Gansu Province(21JR7RA605)
甘肃省自然科学基金(21JR7RA605)
ICON Scientific Research Fund of China Red Cross Foundation(XM_HR_ICON_2021_05)
中国红十字基金会“ICON科研基金”(XM_HR_ICON_2021_05)
作者信息
    1甘肃中医药大学第一临床医学院，甘肃兰州 730000
    2甘肃省人民医院放射科，甘肃兰州 730000
    3飞利浦医疗临床科研部，上海 200070

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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