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首页|期刊导航|临床医学工程|多层螺旋CT平扫联合三期增强扫描在肺癌诊断中的应用价值

多层螺旋CT平扫联合三期增强扫描在肺癌诊断中的应用价值

李鹏政 李卫星 张冰凌 宋瑞敏 盛俊卿 程保国

临床医学工程2024,Vol.31Issue(12):1433-1434,2.
临床医学工程2024,Vol.31Issue(12):1433-1434,2.DOI:10.3969/j.issn.1674-4659.2024.12.1433

多层螺旋CT平扫联合三期增强扫描在肺癌诊断中的应用价值

Application Value of Multi-Slice Spiral CT Plain Scan Combined with Three-Phase Enhanced Scan in the Diagnosis of Lung Cancer

李鹏政 1李卫星 1张冰凌 1宋瑞敏 1盛俊卿 1程保国1

作者信息

  • 1. 新乡市中心医院(新乡医学院第四临床学院)CT室,河南 新乡 453000
  • 折叠

摘要

Abstract

Objective To analyze the application value of multi-slice spiral CT(MSCT)plain scan combined with three-phase enhanced scan in the diagnosis of lung cancer.Methods A total of 80 patients with suspected lung cancer admitted to our hospital from July 2020 to August 2022 were selected.All patients underwent MSCT plain scan and three-phase enhanced scan.The results of pathological examination were used as the"gold standard"for diagnosis.The diagnostic results of MSCT plain scan,and MSCT plain scan combined with three-phase enhanced scan were counted,and the diagnostic value of the two for lung cancer was analyzed.Results The sensitivity and accuracy of MSCT plain scan combined with three-phase enhanced scan in the diagnosis of lung cancer were higher than those of MSCT plain scan(P<0.05).The consistency test results showed that the consistency of MSCT plain scan for lung cancer detection results was acceptable(Kappa value=0.537),and the consistency of MSCT plain scan combined with three-phase enhanced scan for lung cancer detection results was good(Kappa value=0.817).Conclusions MSCT plain scan combined with three-phase enhanced scan in the diagnosis of lung cancer has high sensitivity and accuracy,and has high consistency with the results of pathological examination.

关键词

肺癌/多层螺旋CT平扫/三期增强扫描/诊断/一致性

Key words

Lung cancer/Multi-slice spiral CT plain scan/Three-phase enhanced scan/Diagnosis/Consistency

分类

医药卫生

引用本文复制引用

李鹏政,李卫星,张冰凌,宋瑞敏,盛俊卿,程保国..多层螺旋CT平扫联合三期增强扫描在肺癌诊断中的应用价值[J].临床医学工程,2024,31(12):1433-1434,2.

基金项目

河南省医学科技攻关计划联合共建项目"基于多参数CT影像组学联合深度学习无创预测非小细胞癌患者淋巴结转移"(项目编号:LHGJ20230879) (项目编号:LHGJ20230879)

临床医学工程

1674-4659

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