• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

一项基于人群的病例对照研究中肺癌组织病理评估的诊断一致性

Diagnostic agreement in the histopathological evaluation of lung cancer tissue in a population-based case-control study.

作者信息

Stang Andreas, Pohlabeln Hermann, Müller Klaus M, Jahn Ingeborg, Giersiepen Klaus, Jöckel Karl-Heinz

机构信息

Institute of Medical Epidemiology, Biometry and Informatics, Medical Faculty, Martin-Luther-University of Halle-Wittenberg, Magdeburger Str. 27, 06097 Halle, Germany.

出版信息

Lung Cancer. 2006 Apr;52(1):29-36. doi: 10.1016/j.lungcan.2005.11.012. Epub 2006 Feb 14.

DOI:10.1016/j.lungcan.2005.11.012
PMID:16476504
Abstract

Only few studies have compared the agreement of histological lung carcinoma diagnosis of a population-based case series and an independent pathology review. We analyzed data of our population-based lung cancer case-control study to determine the agreement in the histopathological evaluation of lung cancer. Six-hundred and sixty-eight out of 1004 interviewed male and female lung cancer cases were histologically evaluated according to the 1981 WHO classification by regional pathologists and a central pathologist who was blinded to the evaluations of the regional pathologists. The observed agreement was 0.65 with kappa = 0.54 (95% CI: 0.49-0.58). It was highest for small-cell carcinoma (0.94; kappa = 0.82) and lower for squamous-cell carcinoma (0.81; kappa = 0.58) and adenocarcinoma (0.81; kappa = 0.55). Agreement was slightly higher among women than men. The observed agreement among non-smoking cases was 58% as compared to 67% heavy smoking cases. The moderate agreement for squamous-cell and adenocarcinoma complicates epidemiological studies that address these histological subtypes.

摘要

仅有少数研究比较了基于人群的病例系列中组织学肺癌诊断与独立病理学复查之间的一致性。我们分析了基于人群的肺癌病例对照研究的数据,以确定肺癌组织病理学评估中的一致性。在1004名接受访谈的男性和女性肺癌病例中,有668例由地区病理学家和一名对地区病理学家的评估不知情的中心病理学家根据1981年世界卫生组织分类进行了组织学评估。观察到的一致性为0.65,kappa值为0.54(95%置信区间:0.49 - 0.58)。小细胞癌的一致性最高(0.94;kappa = 0.82),鳞状细胞癌(0.81;kappa = 0.58)和腺癌(0.81;kappa = 0.55)的一致性较低。女性中的一致性略高于男性。非吸烟病例中的观察到的一致性为58%,而重度吸烟病例为67%。鳞状细胞癌和腺癌的中度一致性使针对这些组织学亚型的流行病学研究变得复杂。

相似文献

1
Diagnostic agreement in the histopathological evaluation of lung cancer tissue in a population-based case-control study.一项基于人群的病例对照研究中肺癌组织病理评估的诊断一致性
Lung Cancer. 2006 Apr;52(1):29-36. doi: 10.1016/j.lungcan.2005.11.012. Epub 2006 Feb 14.
2
EU-USA pathology panel for uniform diagnosis in randomised controlled trials for HRCT screening in lung cancer.用于肺癌 HRCT 筛查随机对照试验统一诊断的欧美病理专家组。
Eur Respir J. 2006 Dec;28(6):1186-9. doi: 10.1183/09031936.06.00043506. Epub 2006 Aug 9.
3
Role of fine needle aspiration cytology in diagnosis of lung tumours--a study of 100 cases.细针穿刺细胞学检查在肺肿瘤诊断中的作用——100例研究
Indian J Pathol Microbiol. 2007 Jan;50(1):56-8.
4
Identification of occupational cancer risk in British Columbia: a population-based case-control study of 2,998 lung cancers by histopathological subtype.不列颠哥伦比亚省职业性癌症风险的识别:一项基于人群的2998例肺癌组织病理学亚型病例对照研究。
Am J Ind Med. 2009 Mar;52(3):221-32. doi: 10.1002/ajim.20663.
5
Diagnostic assay based on hsa-miR-205 expression distinguishes squamous from nonsquamous non-small-cell lung carcinoma.基于人源微小RNA-205(hsa-miR-205)表达的诊断检测方法可区分肺鳞癌与非鳞非小细胞肺癌。
J Clin Oncol. 2009 Apr 20;27(12):2030-7. doi: 10.1200/JCO.2008.19.4134. Epub 2009 Mar 9.
6
A case-control study of lifestyle and lung cancer associations by histological types.一项关于按组织学类型划分的生活方式与肺癌关联的病例对照研究。
Neoplasma. 2008;55(3):192-9.
7
[Diagnostic variations among eight pathologists in the histologic classification of lung cancer].[八位病理学家在肺癌组织学分类中的诊断差异]
Gan No Rinsho. 1986 May;32(5):458-62.
8
[Diagnostic procedure in patients with suspected lung cancer. Results of combined evaluation by thoracic surgery and pulmonary medicine specialists].[疑似肺癌患者的诊断程序。胸外科和肺科专家联合评估结果]
Ugeskr Laeger. 1998 Jan 5;160(2):166-9.
9
Lung cancer histologic type in the surveillance, epidemiology, and end results registry versus independent review.监测、流行病学和最终结果登记处中的肺癌组织学类型与独立评估。
J Natl Cancer Inst. 2004 Jul 21;96(14):1105-7. doi: 10.1093/jnci/djh189.
10
[Histological evaluation of lung cancer with T2-weighted magnetic resonance images].[利用T2加权磁共振图像对肺癌进行组织学评估]
Nihon Kyobu Shikkan Gakkai Zasshi. 1995 Sep;33(9):973-80.

引用本文的文献

1
Proteogenomic analysis reveals non-small cell lung cancer subtypes predicting chromosome instability, and tumor microenvironment.蛋白质基因组分析揭示非小细胞肺癌亚型与染色体不稳定性和肿瘤微环境的关系。
Nat Commun. 2024 Nov 23;15(1):10164. doi: 10.1038/s41467-024-54434-4.
2
Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer.深度生成式 AI 模型分析循环孤儿非编码 RNA 可实现早期肺癌检测。
Nat Commun. 2024 Nov 21;15(1):10090. doi: 10.1038/s41467-024-53851-9.
3
Machine learning analysis of pathological images to predict 1-year progression-free survival of immunotherapy in patients with small-cell lung cancer.
机器学习分析病理图像预测小细胞肺癌患者免疫治疗 1 年无进展生存率
J Immunother Cancer. 2024 Feb 15;12(2):e007987. doi: 10.1136/jitc-2023-007987.
4
Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data.基于蛋白质相互作用网络和组学数据融合的卷积神经网络进行人类癌症类型预测。
Sci Rep. 2021 Oct 19;11(1):20691. doi: 10.1038/s41598-021-98814-y.
5
Prediction of Target-Drug Therapy by Identifying Gene Mutations in Lung Cancer With Histopathological Stained Image and Deep Learning Techniques.利用组织病理学染色图像和深度学习技术识别肺癌基因突变以预测靶向药物治疗
Front Oncol. 2021 Apr 13;11:642945. doi: 10.3389/fonc.2021.642945. eCollection 2021.
6
Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study.基于深度学习的肺癌及组织病理全切片图像模拟物六分型分类器:一项回顾性研究。
BMC Med. 2021 Mar 29;19(1):80. doi: 10.1186/s12916-021-01953-2.
7
Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.基于深度学习的跨分类揭示了肿瘤组织学图像中保守的空间行为。
Nat Commun. 2020 Dec 11;11(1):6367. doi: 10.1038/s41467-020-20030-5.
8
A narrative review of digital pathology and artificial intelligence: focusing on lung cancer.数字病理学与人工智能的叙述性综述:聚焦于肺癌
Transl Lung Cancer Res. 2020 Oct;9(5):2255-2276. doi: 10.21037/tlcr-20-591.
9
Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.使用卷积神经网络对非小细胞肺癌进行分类和转录组亚型分析。
J Am Med Inform Assoc. 2020 May 1;27(5):757-769. doi: 10.1093/jamia/ocz230.
10
A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas.用于肺鳞癌和腺癌组织学分型重新分类的转录组学特征分析。
BMC Genomics. 2019 Nov 21;20(1):881. doi: 10.1186/s12864-019-6086-2.