基于CT图像的纹理分析在膀胱癌不同病理级别鉴别中的价值

目的 探讨CT图像纹理分析方法在鉴别膀胱尿路上皮癌不同病理学级别中的价值.方法 回顾性分析43例经术后病理证实的膀胱癌患者的53个病灶,其中高级别尿路上皮癌(HGUC)27个,低级别尿路上皮癌(LGUC)26个.所有患者在同一台CT机上,以同样的扫描参数进行盆腔CT平扫和增强扫描.2名影像科医师分别利用软件工具在CT平扫和增强图像上对病灶进行勾画,并获得感兴趣容积(VOI),生成基于特征类的92个参数,取2名影像科医师测量数据的平均值.采用非参数检验筛选HGUC组和LGUC组间的差异参数,绘制其受试者工作特征(ROC)曲线,确定差异参数的最佳界值,并对诊断效果进行评价.结果 筛选出HGUC组和...

Full description

Saved in:
Bibliographic Details
Published in中华肿瘤杂志 Vol. 40; no. 5; pp. 379 - 383
Main Authors 刘震昊, 石家源, 王海屹, 叶慧义, 王湛博, 杨铁, 马鑫, 白旭
Format Journal Article
LanguageChinese
Published 046000,山西省长治市中医研究所附属医院影像科%陕西省森林工业职工医院CT磁共振室, 西安,710300%解放军总医院放射诊断科, 北京,100853%解放军总医院病理科, 北京,100853%解放军总医院泌尿外科, 北京,100853 2018
Subjects
Histological grading
spiral computed
Urinary bladder neoplasms
膀胱肿瘤
定量纹理分析
体层摄影术,螺旋计算机
Tomography
组织学分级
Diagnosis
诊断,鉴别
Quantitative texture analysis
differential
Online AccessGet full text

Cover

Abstract 目的 探讨CT图像纹理分析方法在鉴别膀胱尿路上皮癌不同病理学级别中的价值.方法 回顾性分析43例经术后病理证实的膀胱癌患者的53个病灶,其中高级别尿路上皮癌(HGUC)27个,低级别尿路上皮癌(LGUC)26个.所有患者在同一台CT机上,以同样的扫描参数进行盆腔CT平扫和增强扫描.2名影像科医师分别利用软件工具在CT平扫和增强图像上对病灶进行勾画,并获得感兴趣容积(VOI),生成基于特征类的92个参数,取2名影像科医师测量数据的平均值.采用非参数检验筛选HGUC组和LGUC组间的差异参数,绘制其受试者工作特征(ROC)曲线,确定差异参数的最佳界值,并对诊断效果进行评价.结果 筛选出HGUC组和LGUC组间的差异纹理参数9个,其中平扫图像参数5个,分别是偏度、均方根、集群阴暗度、区域百分比和大面积高灰度增强;增强图像参数4个,分别是偏度、峰度、集群阴暗度和区域百分比.根据平扫图像VOI获得偏度的曲线下面积最大,为0.840±0.058(95％可信区间:0.726～0.955).偏度的最佳界值为0.1865,诊断HGUC的敏感度为92.59％,特异度为73.08％,阳性预测值为78.13％,阴性预测值为90.48％,准确性为83.02％.结论 基于CT图像的纹理分析方法可有效区分膀胱LGUC与HGUC,其中偏度这一参数诊断效能最佳.
AbstractList 目的 探讨CT图像纹理分析方法在鉴别膀胱尿路上皮癌不同病理学级别中的价值.方法 回顾性分析43例经术后病理证实的膀胱癌患者的53个病灶,其中高级别尿路上皮癌(HGUC)27个,低级别尿路上皮癌(LGUC)26个.所有患者在同一台CT机上,以同样的扫描参数进行盆腔CT平扫和增强扫描.2名影像科医师分别利用软件工具在CT平扫和增强图像上对病灶进行勾画,并获得感兴趣容积(VOI),生成基于特征类的92个参数,取2名影像科医师测量数据的平均值.采用非参数检验筛选HGUC组和LGUC组间的差异参数,绘制其受试者工作特征(ROC)曲线,确定差异参数的最佳界值,并对诊断效果进行评价.结果 筛选出HGUC组和LGUC组间的差异纹理参数9个,其中平扫图像参数5个,分别是偏度、均方根、集群阴暗度、区域百分比和大面积高灰度增强;增强图像参数4个,分别是偏度、峰度、集群阴暗度和区域百分比.根据平扫图像VOI获得偏度的曲线下面积最大,为0.840±0.058(95％可信区间:0.726～0.955).偏度的最佳界值为0.1865,诊断HGUC的敏感度为92.59％,特异度为73.08％,阳性预测值为78.13％,阴性预测值为90.48％,准确性为83.02％.结论 基于CT图像的纹理分析方法可有效区分膀胱LGUC与HGUC,其中偏度这一参数诊断效能最佳.
Abstract_FL Objective To explore the value of CT texture analysis ( CTTA) in differentiating the pathological grade of urothelial carcinoma of the bladder ( UCB) . Methods A total of 53 lesions from 43 patients with bladder cancer confirmed by postoperative pathology were retrospectively analyzed, including 27 cases of high-grade urothelial carcinoma ( HGUC) and 26 cases of low-grade urothelial carcinoma ( LGUC) . All the patients took pelvic CT and enhanced scanning in the same CT scanner with same scanning parameters. Lesions on both plain and enhanced CT images were delineated on software by two radiologists to extract the corresponding volumes of interest ( VOI) and then 92 parameters based on feature classes were generated. The average values of two radiologists were obtained. The difference parameters between HGUC group and LGUC group were screened by nonparametric test, and the receiver operating characteristic ( ROC ) was drawn. The corresponding optimal thresholds were determined and diagnostic effect was assessed. Results Nine difference texture parameters between HGUC group and LGUC group were selected, including 5 parameters on unenhanced images, namely, skewness, root mean squared, cluster shade, zone percentage and large area high gray level emphasis. There were 4 parameters on enhanced images, namely, skewness, kurtosis, cluster shade and zone percentage. The largest area under curve of 0.840±0.058 (95％CI 0.726-0.955) was obtained from skewness generated by VOI of unenhanced images. The cut-off value of skewness was 0. 1865, which permitted the diagnosis of HGUC with sensitivity of 92. 59％, specificity of 73.08％, positive predictive value of 78.13％, negative predictive value of 90.48％ and accuracy of 83.02％. Conclusion CTTA can effectively distinguish between LGUC and HGUC. Skewness from unenhanced CT images had the optimal diagnostic performance.
Author 石家源
王海屹
杨铁
王湛博
马鑫
刘震昊
白旭
叶慧义
AuthorAffiliation 046000,山西省长治市中医研究所附属医院影像科%陕西省森林工业职工医院CT磁共振室, 西安,710300%解放军总医院放射诊断科, 北京,100853%解放军总医院病理科, 北京,100853%解放军总医院泌尿外科, 北京,100853
AuthorAffiliation_xml – name: 046000,山西省长治市中医研究所附属医院影像科%陕西省森林工业职工医院CT磁共振室, 西安,710300%解放军总医院放射诊断科, 北京,100853%解放军总医院病理科, 北京,100853%解放军总医院泌尿外科, 北京,100853
Author_FL Shi Jiayuan
Yang Tie
Wang Haiyi
Wang Zhanbo
Bai Xu
Ma Xin
Liu Zhenhao
Ye Huiyi
Author_FL_xml – sequence: 1
  fullname: Liu Zhenhao
– sequence: 2
  fullname: Shi Jiayuan
– sequence: 3
  fullname: Wang Haiyi
– sequence: 4
  fullname: Ye Huiyi
– sequence: 5
  fullname: Wang Zhanbo
– sequence: 6
  fullname: Yang Tie
– sequence: 7
  fullname: Ma Xin
– sequence: 8
  fullname: Bai Xu
Author_xml – sequence: 1
  fullname: 刘震昊
– sequence: 2
  fullname: 石家源
– sequence: 3
  fullname: 王海屹
– sequence: 4
  fullname: 叶慧义
– sequence: 5
  fullname: 王湛博
– sequence: 6
  fullname: 杨铁
– sequence: 7
  fullname: 马鑫
– sequence: 8
  fullname: 白旭
BookMark eNrjYmDJy89LZWDQMDTQMzY3M9BPzk3Uy9LLLC7O0zMwMjXWBQqa6RkZGFroGZjqGRgasjBwwsU5GHiLizOTDEwNjc0sjA3NOBl8n87f9WRXn3PI09n7njb3P5_V8nzXzucT2p52tD2bN-HpnBUv2hpeNG98PrPnyY7epxN6nk9vBco-37X8acfql51bgOSTHWuBup7s3v60YQ8PA2taYk5xKi-U5mZQdXMNcfbQLU_MS0vMS4_Pyi8tygPKxFdlVOWAHGlgCnSiMbHqAPxlY_k
ContentType Journal Article
Copyright Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
Copyright_xml – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
DBID 2B.
4A8
92I
93N
PSX
TCJ
DOI 10.3760/cma.j.issn.0253-3766.2018.05.011
DatabaseName Wanfang Data Journals - Hong Kong
WANFANG Data Centre
Wanfang Data Journals
万方数据期刊 - 香港版
China Online Journals (COJ)
China Online Journals (COJ)
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
DocumentTitle_FL CT texture analysis in bladder carcinoma: histologic grade characterization
EndPage 383
ExternalDocumentID zhzl201805011
GroupedDBID ---
-05
123
2B.
2C~
4A8
92F
92I
93N
ABDBF
ACGFS
ALMA_UNASSIGNED_HOLDINGS
CCEZO
CIEJG
CW9
EOJEC
OBODZ
PSX
TCJ
TGQ
U1G
U5O
ID FETCH-wanfang_journals_zhzl2018050113
ISSN 0253-3766
IngestDate Tue Feb 13 23:18:32 EST 2024
IsPeerReviewed false
IsScholarly true
Issue 5
Keywords Histological grading
spiral computed
Urinary bladder neoplasms
膀胱肿瘤
定量纹理分析
体层摄影术,螺旋计算机
Tomography
组织学分级
Diagnosis
诊断,鉴别
Quantitative texture analysis
differential
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-wanfang_journals_zhzl2018050113
ParticipantIDs wanfang_journals_zhzl201805011
PublicationCentury 2000
PublicationDate 2018
PublicationDateYYYYMMDD 2018-01-01
PublicationDate_xml – year: 2018
  text: 2018
PublicationDecade 2010
PublicationTitle 中华肿瘤杂志
PublicationTitle_FL Chinese Journal of Oncology
PublicationYear 2018
Publisher 046000,山西省长治市中医研究所附属医院影像科%陕西省森林工业职工医院CT磁共振室, 西安,710300%解放军总医院放射诊断科, 北京,100853%解放军总医院病理科, 北京,100853%解放军总医院泌尿外科, 北京,100853
Publisher_xml – name: 046000,山西省长治市中医研究所附属医院影像科%陕西省森林工业职工医院CT磁共振室, 西安,710300%解放军总医院放射诊断科, 北京,100853%解放军总医院病理科, 北京,100853%解放军总医院泌尿外科, 北京,100853
SSID ssib051368316
ssib007279245
ssib000995398
ssj0042033
ssib017477325
ssib006576341
ssib001103529
Score 4.4123344
Snippet 目的 探讨CT图像纹理分析方法在鉴别膀胱尿路上皮癌不同病理学级别中的价值.方法...
SourceID wanfang
SourceType Aggregation Database
StartPage 379
Title 基于CT图像的纹理分析在膀胱癌不同病理级别鉴别中的价值
URI https://d.wanfangdata.com.cn/periodical/zhzl201805011
Volume 40
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3NaxQxFB9KK-JFFBU_Sw8GvLRmZ5KZ5JiZnaUI9bRCb2Vnt2spuoK2lz0VqqVC1d7ED_SoIB4EwbqKf82OW_8L33vJ7E5LsVXwMoSXl7zfey_ZvDebSTzvarAoGn4zbOPuKT4tpGxNNzRMPNnOsrDREm1f47fDczfD2VvixrycHzs2Udq1tLqSzTS7B35X8i9eBRr4Fb-S_QvPDjsFApTBv_AED8PzSD5mqWS6xmLDUoFPlSZ1osUsTrGgAqZqLI2YhkqBBeCKNVE4UyHxKCqETKdIxOYJM4qlRFecCgGLK9RKM5WQNCBWiZkTBaoipmSpZ5JlIifCxCyFtprFokShfky1hBAoAN624szdu71cqDvklyhdpYTNZ7HVEaoEKVJFIvAAXQ_XWScWuAAIqKgi4oUOzIglInsGyGtSFpNdQA3NyywgV8VUIx1UME6s9wiqucZgErSBQKvbW4yKdyxuQcD5gH8b423ZSdFXBRUzslAMwFYItaWAYB_NhMyk_x67JFQVMcOZJpcbXWDR5E5NXhROlk5LrajKWNtVCUZpDIEeGtY96dgAzAEgQ2YCNJ0bT9Y6EbJZkOBm9FmFBs2waj8AHMYR9qQsBEKKGsDIrDnngK_h56GEgaigo59EeKsdJ6gKYUNHiEcUsyLEGZLS4LZm3K9-iTnmZCuFgpWhqtANWWcQBEFd6MjNQ5MAFc-zwste_hnEQVPqP4sMcew7z_DCqAI1PqLsUlzhywDjirAcBNkz09yPvSxFNIG968kFx4G9dWp_3IU76-DHsnm3MbNMQmaGQnD3rLKHI1dGMedwJ3B3qXsHObjkeELDhB9pKca9CRNX41opD9SyfE4p5CyQOI7ysFBCmFTK6yI8FnW0XaISiSgKRoGLrAShCjAvtiG78HlAW5EK0Me9a06r64fpRB97dtqNzu1SXlI_5Z10LxSmjF0dTntj3aUz3lz-ttfvPU3q-asf-fqzwcuHg97XwfZGvrnx8812_vr97sba7vqnwYut_s6TfHtr8PwR1A567_LND78ef4Znf-cjtOp_-5KvfT_rsVpaT2anHYQFtzg9WNhj1-CcN96511k87021NM_CgPvcb2RCQ3aaNaTIhGy2Wn7L5-KCN_nnvi4exnDJO4Fl-5r6sje-cn918QokbivZpHPrbywBGgk
link.rule.ids 315,786,790,4043,27954,27955,27956
linkProvider EBSCOhost
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8ECT%E5%9B%BE%E5%83%8F%E7%9A%84%E7%BA%B9%E7%90%86%E5%88%86%E6%9E%90%E5%9C%A8%E8%86%80%E8%83%B1%E7%99%8C%E4%B8%8D%E5%90%8C%E7%97%85%E7%90%86%E7%BA%A7%E5%88%AB%E9%89%B4%E5%88%AB%E4%B8%AD%E7%9A%84%E4%BB%B7%E5%80%BC&rft.jtitle=%E4%B8%AD%E5%8D%8E%E8%82%BF%E7%98%A4%E6%9D%82%E5%BF%97&rft.au=%E5%88%98%E9%9C%87%E6%98%8A&rft.au=%E7%9F%B3%E5%AE%B6%E6%BA%90&rft.au=%E7%8E%8B%E6%B5%B7%E5%B1%B9&rft.au=%E5%8F%B6%E6%85%A7%E4%B9%89&rft.date=2018&rft.pub=046000%2C%E5%B1%B1%E8%A5%BF%E7%9C%81%E9%95%BF%E6%B2%BB%E5%B8%82%E4%B8%AD%E5%8C%BB%E7%A0%94%E7%A9%B6%E6%89%80%E9%99%84%E5%B1%9E%E5%8C%BB%E9%99%A2%E5%BD%B1%E5%83%8F%E7%A7%91%25%E9%99%95%E8%A5%BF%E7%9C%81%E6%A3%AE%E6%9E%97%E5%B7%A5%E4%B8%9A%E8%81%8C%E5%B7%A5%E5%8C%BB%E9%99%A2CT%E7%A3%81%E5%85%B1%E6%8C%AF%E5%AE%A4%2C+%E8%A5%BF%E5%AE%89%2C710300%25%E8%A7%A3%E6%94%BE%E5%86%9B%E6%80%BB%E5%8C%BB%E9%99%A2%E6%94%BE%E5%B0%84%E8%AF%8A%E6%96%AD%E7%A7%91%2C+%E5%8C%97%E4%BA%AC%2C100853%25%E8%A7%A3%E6%94%BE%E5%86%9B%E6%80%BB%E5%8C%BB%E9%99%A2%E7%97%85%E7%90%86%E7%A7%91%2C+%E5%8C%97%E4%BA%AC%2C100853%25%E8%A7%A3%E6%94%BE%E5%86%9B%E6%80%BB%E5%8C%BB%E9%99%A2%E6%B3%8C%E5%B0%BF%E5%A4%96%E7%A7%91%2C+%E5%8C%97%E4%BA%AC%2C100853&rft.issn=0253-3766&rft.volume=40&rft.issue=5&rft.spage=379&rft.epage=383&rft_id=info:doi/10.3760%2Fcma.j.issn.0253-3766.2018.05.011&rft.externalDocID=zhzl201805011
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fzhzl%2Fzhzl.jpg