• 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

以计算机辅助检测作为第二阅片者的CT结肠成像:观察者性能研究

CT colonography with computer-aided detection as a second reader: observer performance study.

作者信息

Petrick Nicholas, Haider Maruf, Summers Ronald M, Yeshwant Srinath C, Brown Linda, Iuliano Edward M, Louie Adeline, Choi J Richard, Pickhardt Perry J

机构信息

National Institute of Biomedical Imaging and Bioengineering/Center for Devices and Radiological Health Joint Laboratory for the Assessment of Medical Imaging Systems, U.S. Food and Drug Administration, Rockville, MD, USA.

出版信息

Radiology. 2008 Jan;246(1):148-56. doi: 10.1148/radiol.2453062161.

DOI:10.1148/radiol.2453062161
PMID:18096536
Abstract

PURPOSE

To evaluate the effect of computer-aided detection (CAD) as second reader on radiologists' diagnostic performance in interpreting computed tomographic (CT) colonographic examinations by using a primary two-dimensional (2D) approach, with segmental, unblinded optical colonoscopy as the reference standard.

MATERIALS AND METHODS

This HIPAA-compliant study was IRB-approved with written informed consent. Four board-certified radiologists analyzed 60 CT examinations with a commercially available review system. Two-dimensional transverse views were used for initial polyp detection, while three-dimensional (3D) endoluminal and 2D multiplanar views were available for problem solving. After initial review without CAD, the reader was shown CAD-identified polyp candidates. The readers were then allowed to add to or modify their original diagnoses. Polyp location, CT Colonography Reporting and Data System categorization, and reader confidence as to the likelihood of a candidate being a polyp were recorded before and after CAD reading. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity were estimated for CT examinations with and without CAD readings by using multireader multicase analysis.

RESULTS

Use of CAD led to nonsignificant average reader AUC increases of 0.03, 0.03, and 0.04 for patients with adenomatous polyps 6 mm or larger, 6-9 mm, and 10 mm or larger, respectively (P > or = .25); likewise, CAD increased average reader sensitivity by 0.15, 0.16, and 0.14 for those respective groups, with a corresponding decrease in specificity of 0.14. These changes achieved significance for the 6 mm or larger group (P < .01), 6-9 mm group (P < .02), and for specificity (P < .01), but not for the 10 mm or larger group (P > .16). The average reading time was 5.1 minutes +/- 3.4 (standard deviation) without CAD. CAD added an average of 3.1 minutes +/- 4.3 (62%) to each reading (supine and prone positions combined); average total reading time, 8.2 minutes +/- 5.8.

CONCLUSION

Use of CAD led to a significant increase in sensitivity for detecting polyps in the 6 mm or larger and 6-9 mm groups at the expense of a similar significant reduction in specificity.

摘要

目的

以分段、非盲法光学结肠镜检查作为参考标准,评估计算机辅助检测(CAD)作为第二阅片者对放射科医生解读计算机断层扫描(CT)结肠成像检查的诊断性能的影响,采用主要的二维(2D)方法。

材料与方法

本符合健康保险流通与责任法案(HIPAA)的研究经机构审查委员会(IRB)批准并获得书面知情同意。四位获得委员会认证的放射科医生使用商用审查系统分析了60例CT检查。二维横断面视图用于初始息肉检测,而三维(3D)腔内视图和二维多平面视图用于解决问题。在无CAD的初始审查后,向阅片者展示CAD识别出的息肉候选者。然后允许阅片者补充或修改其原始诊断。记录CAD阅片前后息肉位置、CT结肠成像报告和数据系统分类以及阅片者对候选者为息肉可能性的信心。使用多阅片者多病例分析估计有无CAD阅片的CT检查的受试者工作特征(ROC)曲线下面积(AUC)、敏感性和特异性。

结果

对于直径6mm及以上、6 - 9mm和10mm及以上的腺瘤性息肉患者,使用CAD导致阅片者的平均AUC分别非显著增加0.03、0.03和0.04(P≥0.25);同样,对于这些相应组,CAD使阅片者的平均敏感性分别增加0.15、0.16和0.14,特异性相应降低0.14。这些变化在直径6mm及以上组(P < 0.01)、6 - 9mm组(P < 0.02)以及特异性方面(P < 0.01)达到显著水平,但在直径10mm及以上组未达到显著水平(P > 0.16)。无CAD时平均阅读时间为5.1分钟±3.4(标准差)。CAD使每次阅读(仰卧位和俯卧位合并)平均增加3.1分钟±4.3(62%);平均总阅读时间为8.2分钟±5.8。

结论

使用CAD导致直径6mm及以上和6 - 9mm组检测息肉的敏感性显著增加,但特异性也有类似的显著降低。

相似文献

1
CT colonography with computer-aided detection as a second reader: observer performance study.以计算机辅助检测作为第二阅片者的CT结肠成像:观察者性能研究
Radiology. 2008 Jan;246(1):148-56. doi: 10.1148/radiol.2453062161.
2
Computer-aided detection of colorectal polyps: can it improve sensitivity of less-experienced readers? Preliminary findings.计算机辅助检测结肠息肉:它能提高经验较少的阅片者的敏感度吗?初步研究结果。
Radiology. 2007 Oct;245(1):140-9. doi: 10.1148/radiol.2451061116.
3
Effect of computer-aided detection for CT colonography in a multireader, multicase trial.多读者、多病例试验中 CT 结肠成像计算机辅助检测的效果。
Radiology. 2010 Sep;256(3):827-35. doi: 10.1148/radiol.10091890. Epub 2010 Jul 27.
4
CT colonography: influence of 3D viewing and polyp candidate features on interpretation with computer-aided detection.CT结肠成像:三维观察和息肉候选特征对计算机辅助检测解读的影响
Radiology. 2006 Jun;239(3):768-76. doi: 10.1148/radiol.2393050418.
5
CT colonography: investigation of the optimum reader paradigm by using computer-aided detection software.CT结肠成像:使用计算机辅助检测软件对最佳阅片模式的研究
Radiology. 2008 Feb;246(2):463-71. doi: 10.1148/radiol.2461070190. Epub 2007 Dec 19.
6
Polyp detection with CT colonography: primary 3D endoluminal analysis versus primary 2D transverse analysis with computer-assisted reader software.CT结肠成像检测息肉:使用计算机辅助阅片软件进行的原发性三维腔内分析与原发性二维横断面分析
Radiology. 2006 Jun;239(3):759-67. doi: 10.1148/radiol.2392050483. Epub 2006 Mar 16.
7
CT colonography and computer-aided detection: effect of false-positive results on reader specificity and reading efficiency in a low-prevalence screening population.CT结肠成像与计算机辅助检测:低患病率筛查人群中假阳性结果对阅片者特异性及阅片效率的影响
Radiology. 2008 Apr;247(1):133-40. doi: 10.1148/radiol.2471070816. Epub 2008 Feb 21.
8
Virtual dissection CT colonography: evaluation of learning curves and reading times with and without computer-aided detection.虚拟解剖CT结肠成像:有无计算机辅助检测情况下学习曲线和阅片时间的评估
Radiology. 2008 Sep;248(3):860-8. doi: 10.1148/radiol.2482070895.
9
Colonic polyps: complementary role of computer-aided detection in CT colonography.结肠息肉:计算机辅助检测在CT结肠成像中的辅助作用
Radiology. 2002 Nov;225(2):391-9. doi: 10.1148/radiol.2252011619.
10
CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography.CT结肠成像:计算机辅助检测结肠息肉对具有中等CT结肠成像经验的普通放射科医生作为第二阅读者和同步阅读者的影响。
Eur Radiol. 2014 Jul;24(7):1466-76. doi: 10.1007/s00330-014-3158-1. Epub 2014 May 10.

引用本文的文献

1
The impact of AI suggestions on radiologists' decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination.人工智能建议对放射科医生决策的影响:一项关于在乳房 X 光检查中可解释性和态度启动干预的试点研究。
Sci Rep. 2023 Jun 7;13(1):9230. doi: 10.1038/s41598-023-36435-3.
2
Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps.人工智能与人类专家在结直肠息肉检测和分类中的表现及比较。
BMC Gastroenterol. 2022 Dec 13;22(1):517. doi: 10.1186/s12876-022-02605-2.
3
The Added Effect of Artificial Intelligence on Physicians' Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review.
人工智能对医生在CT和胸部X光检查中检测胸部病变表现的附加影响:一项系统综述。
Diagnostics (Basel). 2021 Nov 26;11(12):2206. doi: 10.3390/diagnostics11122206.
4
Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers.计算机辅助诊断可提高多参数磁共振成像对临床显著前列腺癌的检测能力:一项涉及无经验阅片者的多观察者性能研究
Diagnostics (Basel). 2021 May 28;11(6):973. doi: 10.3390/diagnostics11060973.
5
Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis.用于皮肤病变诊断的风险感知机器学习分类器
J Clin Med. 2019 Aug 17;8(8):1241. doi: 10.3390/jcm8081241.
6
Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks.使用深度卷积神经网络从多机构多参数磁共振成像中检测前列腺癌。
J Med Imaging (Bellingham). 2018 Oct;5(4):044507. doi: 10.1117/1.JMI.5.4.044507. Epub 2018 Dec 15.
7
Memory bias in observer-performance literature.观察者表现文献中的记忆偏差。
J Med Imaging (Bellingham). 2018 Jul;5(3):031412. doi: 10.1117/1.JMI.5.3.031412. Epub 2018 Sep 24.
8
Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs.基于深度学习的胸部 X 线片活动性肺结核自动检测算法的开发与验证。
Clin Infect Dis. 2019 Aug 16;69(5):739-747. doi: 10.1093/cid/ciy967.
9
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning.基于深度学习的 CT 影像组学膀胱癌治疗反应评估
Sci Rep. 2017 Aug 18;7(1):8738. doi: 10.1038/s41598-017-09315-w.
10
Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study.三维磁共振成像上脑转移瘤的计算机辅助检测:观察者性能研究。
PLoS One. 2017 Jun 8;12(6):e0178265. doi: 10.1371/journal.pone.0178265. eCollection 2017.