• 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

相似文献

1
Pulmonary nodule radiological diagnostic algorithm in lung cancer screening.肺癌筛查中肺结节的放射学诊断算法
Transl Lung Cancer Res. 2021 Feb;10(2):1124-1135. doi: 10.21037/tlcr-20-755.
2
Screen-detected solid nodules: from detection of nodule to structured reporting.筛查发现的实性结节:从结节检测到结构化报告
Transl Lung Cancer Res. 2021 May;10(5):2335-2346. doi: 10.21037/tlcr-20-296.
3
Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening.CT 检测到肺结节患者的肺癌概率:来自低剂量 CT 筛查 NELSON 试验数据的预设分析。
Lancet Oncol. 2014 Nov;15(12):1332-41. doi: 10.1016/S1470-2045(14)70389-4. Epub 2014 Oct 1.
4
The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography.人工智能在低剂量计算机断层扫描肺癌筛查中的潜在作用
Diagnostics (Basel). 2022 Oct 8;12(10):2435. doi: 10.3390/diagnostics12102435.
5
Risk assessment in relation to the detection of small pulmonary nodules.与小肺结节检测相关的风险评估
Transl Lung Cancer Res. 2017 Feb;6(1):35-41. doi: 10.21037/tlcr.2017.02.05.
6
Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography.基于低剂量 CT 检测肺结节的良恶性鉴别预测模型评估。
JAMA Netw Open. 2020 Feb 5;3(2):e1921221. doi: 10.1001/jamanetworkopen.2019.21221.
7
Comparison of National Comprehensive Cancer Network and European Position Statement protocols for nodule management in low-dose computed tomography lung cancer screening in a general Chinese population.中国普通人群低剂量计算机断层扫描肺癌筛查中,美国国立综合癌症网络与欧洲立场声明结节管理方案的比较
J Thorac Dis. 2021 Dec;13(12):6855-6865. doi: 10.21037/jtd-21-1312.
8
Factors Associated with a Positive Baseline Screening Exam Result in the National Lung Screening Trial.与国家肺癌筛查试验中基线筛查检查阳性结果相关的因素。
Ann Am Thorac Soc. 2016 Sep;13(9):1568-74. doi: 10.1513/AnnalsATS.201602-091OC.
9
Lung cancer screening: nodule identification and characterization.肺癌筛查:结节的识别与特征描述
Transl Lung Cancer Res. 2018 Jun;7(3):288-303. doi: 10.21037/tlcr.2018.05.02.
10
Trade-off between benefits, harms and economic efficiency of low-dose CT lung cancer screening: a microsimulation analysis of nodule management strategies in a population-based setting.低剂量CT肺癌筛查的益处、危害与经济效率之间的权衡:基于人群的结节管理策略微观模拟分析
BMC Med. 2017 Aug 25;15(1):162. doi: 10.1186/s12916-017-0924-3.

引用本文的文献

1
Comparison of Lung-RADS Version 2022 and British Thoracic Society Guidelines in Classifying Solid Pulmonary Nodules Detected at Lung Cancer Screening CT.肺癌筛查CT检出实性肺结节的Lung-RADS 2022版与英国胸科学会指南的比较
Life (Basel). 2024 Dec 27;15(1):14. doi: 10.3390/life15010014.
2
The Solid Volume Ratio is Better Than the Consolidation Tumor Ratio in Predicting the Malignant Pathological Features of cT1 Lung Adenocarcinoma.实性体积比在预测cT1期肺腺癌的恶性病理特征方面优于实性肿瘤比。
Thorac Cardiovasc Surg. 2025 Jun;73(4):308-316. doi: 10.1055/a-2380-6799. Epub 2024 Aug 6.
3
Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation.基于计算机断层扫描(CT)密度的人工智能在肺良恶性结节中的诊断价值:一项回顾性研究。
PeerJ. 2024 Jan 2;12:e16577. doi: 10.7717/peerj.16577. eCollection 2024.
4
See Lung Cancer with an AI.借助人工智能观察肺癌。
Cancers (Basel). 2023 Feb 19;15(4):1321. doi: 10.3390/cancers15041321.

本文引用的文献

1
ESR/ERS statement paper on lung cancer screening.ESR/ERS关于肺癌筛查的声明文件。
Eur Respir J. 2020 Feb 12;55(2). doi: 10.1183/13993003.00506-2019. Print 2020 Feb.
2
Protocol and Rationale for the International Lung Screening Trial.国际肺癌筛查试验的方案和原理。
Ann Am Thorac Soc. 2020 Apr;17(4):503-512. doi: 10.1513/AnnalsATS.201902-102OC.
3
Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial.随机试验中 CT 容积筛查降低肺癌死亡率
N Engl J Med. 2020 Feb 6;382(6):503-513. doi: 10.1056/NEJMoa1911793. Epub 2020 Jan 29.
4
Implementation of lung cancer screening at the national level: Polish example.国家层面肺癌筛查的实施:波兰的例子。
Transl Lung Cancer Res. 2019 May;8(Suppl 1):S95-S105. doi: 10.21037/tlcr.2019.03.09.
5
Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial.使用连续容积筛查 12 个月检测肺结节中的癌症概率:英国纵向研究。
Thorax. 2019 Aug;74(8):761-767. doi: 10.1136/thoraxjnl-2018-212263. Epub 2019 Apr 26.
6
Overdiagnosis: "A Malformed Concept".过度诊断:“一个畸形的概念”。
J Thorac Imaging. 2019 May;34(3):151-153. doi: 10.1097/RTI.0000000000000408.
7
European position statement on lung cancer screening.欧洲肺癌筛查立场声明。
Lancet Oncol. 2017 Dec;18(12):e754-e766. doi: 10.1016/S1470-2045(17)30861-6.
8
Quantification of growth patterns of screen-detected lung cancers: The NELSON study.筛查发现的肺癌生长模式的量化:NELSON研究。
Lung Cancer. 2017 Jun;108:48-54. doi: 10.1016/j.lungcan.2017.02.021. Epub 2017 Mar 1.
9
Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?肺-放射学报告和数据系统(Lung-RADS)类别 4X:它是否能提高亚实性结节恶性肿瘤的预测准确性?
Radiology. 2017 Jul;284(1):264-271. doi: 10.1148/radiol.2017161624. Epub 2017 Mar 24.
10
Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017.CT 图像上偶然发现的肺结节管理指南:来自 2017 年 Fleischner 学会。
Radiology. 2017 Jul;284(1):228-243. doi: 10.1148/radiol.2017161659. Epub 2017 Feb 23.

肺癌筛查中肺结节的放射学诊断算法

Pulmonary nodule radiological diagnostic algorithm in lung cancer screening.

作者信息

Dziadziuszko Katarzyna, Szurowska Edyta

机构信息

II Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland.

出版信息

Transl Lung Cancer Res. 2021 Feb;10(2):1124-1135. doi: 10.21037/tlcr-20-755.

DOI:10.21037/tlcr-20-755
PMID:33718050
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7947388/
Abstract

Publications of the final results of the two largest randomized lung cancer screening (LCS) trials in the United States and Europe confirmed the reduction in the mortality rate associated with the use of screening with low-dose computed tomography (LDCT). Results of these trials led to widespread acceptance of LCS in properly defined high-risk populations, and its implementation in the clinical practice. Many countries started preparation for national LCS and refreshed still open debate about lung nodule management. Detection of lung cancer in the early stage with a reduction of lung cancer mortality requires dedicated programs with optimized protocols, including a specified pulmonary nodule diagnostic algorithm. The screening protocol should be clear with a precise nodule diameter or volume threshold, based on which a positive screen result is defined. The application of risk-prediction models and the introduction of the semiautomated assessment of detected nodules improves screening accuracy and should be applied in LCS protocols as verified tools to aid radiological diagnosis. In this review, we have summarized recent data about the radiological protocols from the most important LCS programs and pulmonary diagnostic algorithms. These protocols should be taken into consideration in the ongoing and planned LCS programs.

摘要

美国和欧洲两项最大规模的随机肺癌筛查(LCS)试验最终结果的公布,证实了使用低剂量计算机断层扫描(LDCT)进行筛查可降低死亡率。这些试验结果使得LCS在明确界定的高危人群中得到广泛认可,并在临床实践中得以实施。许多国家开始为全国性LCS做准备,并重新引发了关于肺结节管理的讨论。通过专门的项目和优化方案,包括特定的肺结节诊断算法,实现肺癌的早期检测并降低肺癌死亡率。筛查方案应明确,有精确的结节直径或体积阈值,据此定义阳性筛查结果。风险预测模型的应用以及对检测到的结节进行半自动评估,提高了筛查准确性,应作为经过验证的辅助放射诊断工具应用于LCS方案中。在本综述中,我们总结了来自最重要的LCS项目关于放射学方案和肺部诊断算法的最新数据。在正在进行和计划中的LCS项目中应考虑这些方案。