• 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

英国肺癌筛查(UKLS):前 88897 例入组人群的人口学特征为人群筛查提供了建议。

The UK Lung Screen (UKLS): demographic profile of first 88,897 approaches provides recommendations for population screening.

机构信息

Roy Castle Lung Cancer Research Programme, The University of Liverpool Cancer Research Centre, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, The University of Liverpool, Roy Castle Building, 200 London Road, Liverpool L3 9TA, United Kingdom.

出版信息

Cancer Prev Res (Phila). 2014 Mar;7(3):362-71. doi: 10.1158/1940-6207.CAPR-13-0206. Epub 2014 Jan 17.

DOI:10.1158/1940-6207.CAPR-13-0206
PMID:24441672
Abstract

UNLABELLED

The UK Lung Cancer Screening trial (UKLS) aims to evaluate low-dose computed tomography (LDCT) lung cancer population screening in the United Kingdom. In UKLS, a large population sample ages 50 to 75 years is approached with a questionnaire to determine lung cancer risk. Those with an estimated risk of at least 5% of developing lung cancer in the next 5 years (using the Liverpool Lung project risk model) are invited to participate in the trial. Here, we present demographic, risk, and response rate data from the first 88,897 individuals approached. Of note, 23,794 individuals (26.8% of all approached) responded positively to the initial questionnaire; 12% of these were high risk. Higher socioeconomic status correlated positively with response, but inversely with risk (P < 0.001). The 50- to 55-year age group was least likely to participate, and at lowest cancer risk. Only 5% of clinic attendees were ages ≤60 years (compared with 47% of all 88,897 approached); this has implications for cost effectiveness. Among positive responders, there were more ex-smokers than expected from population figures (40% vs. 33%), and fewer current smokers (14% vs. 17.5%). Of note, 32.7% of current smokers and 18.4% of ex-smokers were designated as high risk. Overall, 1,452 of 23,794 positive responders (6.1%) were deemed high risk and attended a recruitment clinic. UKLS is the first LDCT population screening trial, selecting high-risk subjects using a validated individual risk prediction model.

KEY FINDINGS

(i) better recruitment from ex- rather than current smokers, (ii) few clinic attendees ages early 50s, and (iii) representative number of socioeconomically deprived people recruited, despite lower response rates.

摘要

未加标签

英国肺癌筛查试验(UKLS)旨在评估英国低剂量计算机断层扫描(LDCT)肺癌人群筛查。在 UKLS 中,对年龄在 50 至 75 岁之间的大量人群样本进行问卷调查,以确定肺癌风险。那些在未来 5 年内患肺癌的风险估计至少为 5%(使用利物浦肺项目风险模型)的人被邀请参加试验。在这里,我们展示了前 88897 名被调查者的人口统计学、风险和响应率数据。值得注意的是,23794 名(所有被调查者的 26.8%)对初始问卷做出了积极回应;其中 12%为高风险。较高的社会经济地位与响应呈正相关,但与风险呈负相关(P<0.001)。50 至 55 岁年龄组参与度最低,患癌风险最低。只有 5%的诊所就诊者年龄≤60 岁(而所有 88897 名被调查者中有 47%);这对成本效益有影响。在阳性反应者中,吸烟者比例高于人口统计数据(40%比 33%),而当前吸烟者比例较低(14%比 17.5%)。值得注意的是,32.7%的当前吸烟者和 18.4%的前吸烟者被指定为高风险。总的来说,在 23794 名阳性反应者中,有 1452 名(6.1%)被认为是高风险,并参加了招募诊所。UKLS 是第一个 LDCT 人群筛查试验,使用经过验证的个体风险预测模型选择高风险受试者。

主要发现

(i)招募更多的前吸烟者而非当前吸烟者,(ii)50 岁出头的诊所就诊者人数较少,(iii)尽管响应率较低,但仍招募了相当数量的社会经济贫困人群。

相似文献

1
The UK Lung Screen (UKLS): demographic profile of first 88,897 approaches provides recommendations for population screening.英国肺癌筛查(UKLS):前 88897 例入组人群的人口学特征为人群筛查提供了建议。
Cancer Prev Res (Phila). 2014 Mar;7(3):362-71. doi: 10.1158/1940-6207.CAPR-13-0206. Epub 2014 Jan 17.
2
The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer.英国肺癌筛查试验:一项关于低剂量计算机断层扫描筛查早期肺癌的试点随机对照试验。
Health Technol Assess. 2016 May;20(40):1-146. doi: 10.3310/hta20400.
3
Barriers to uptake among high-risk individuals declining participation in lung cancer screening: a mixed methods analysis of the UK Lung Cancer Screening (UKLS) trial.高危个体参与肺癌筛查意愿下降的阻碍因素:英国肺癌筛查(UKLS)试验的混合方法分析
BMJ Open. 2015 Jul 14;5(7):e008254. doi: 10.1136/bmjopen-2015-008254.
4
UK Lung Cancer RCT Pilot Screening Trial: baseline findings from the screening arm provide evidence for the potential implementation of lung cancer screening.英国肺癌随机对照试验试点筛查试验:筛查组的基线结果为肺癌筛查的潜在实施提供了证据。
Thorax. 2016 Feb;71(2):161-70. doi: 10.1136/thoraxjnl-2015-207140. Epub 2015 Dec 8.
5
Long-term psychosocial outcomes of low-dose CT screening: results of the UK Lung Cancer Screening randomised controlled trial.低剂量CT筛查的长期社会心理结局:英国肺癌筛查随机对照试验的结果
Thorax. 2016 Nov;71(11):996-1005. doi: 10.1136/thoraxjnl-2016-208283. Epub 2016 Jul 28.
6
Assessing Lung Cancer Absolute Risk Trajectory Based on a Polygenic Risk Model.基于多基因风险模型评估肺癌绝对风险轨迹。
Cancer Res. 2021 Mar 15;81(6):1607-1615. doi: 10.1158/0008-5472.CAN-20-1237. Epub 2021 Jan 20.
7
Predicting Lung Cancer Occurrence in Never-Smoking Females in Asia: TNSF-SQ, a Prediction Model.亚洲不吸烟女性肺癌发病预测:TNSF-SQ 预测模型
Cancer Epidemiol Biomarkers Prev. 2020 Feb;29(2):452-459. doi: 10.1158/1055-9965.EPI-19-1221. Epub 2019 Dec 17.
8
Epidemiology of lung cancer and lung cancer screening programs in China and the United States.中国和美国的肺癌流行病学和肺癌筛查计划。
Cancer Lett. 2020 Jan 1;468:82-87. doi: 10.1016/j.canlet.2019.10.009. Epub 2019 Oct 7.
9
Association of Inclusion of More Black Individuals in Lung Cancer Screening With Reduced Mortality.纳入更多黑人个体进行肺癌筛查与降低死亡率相关。
JAMA Netw Open. 2021 Aug 2;4(8):e2119629. doi: 10.1001/jamanetworkopen.2021.19629.
10
UK Lung Screen (UKLS) nodule management protocol: modelling of a single screen randomised controlled trial of low-dose CT screening for lung cancer.英国肺癌筛查(UKLS)结节管理方案:低剂量 CT 肺癌筛查的单屏随机对照试验建模。
Thorax. 2011 Apr;66(4):308-13. doi: 10.1136/thx.2010.152066. Epub 2011 Feb 11.

引用本文的文献

1
A cost minimization analysis of the implementation of the international lung screening trial in Catalonia (Spain).西班牙加泰罗尼亚地区国际肺癌筛查试验实施的成本最小化分析。
BMC Health Serv Res. 2025 Jul 30;25(1):1001. doi: 10.1186/s12913-025-13008-w.
2
Innovative Approaches to Early Detection of Cancer-Transforming Screening for Breast, Lung, and Hard-to-Screen Cancers.癌症早期检测的创新方法——乳腺癌、肺癌及难筛查癌症的转化性筛查
Cancers (Basel). 2025 Jun 2;17(11):1867. doi: 10.3390/cancers17111867.
3
Eligibility criteria for lung cancer screening in France: a modelling study.
法国肺癌筛查的资格标准:一项建模研究。
Lancet Reg Health Eur. 2025 Jan 31;51:101221. doi: 10.1016/j.lanepe.2025.101221. eCollection 2025 Apr.
4
Co-designing a recruitment strategy for lung cancer screening in high-risk individuals: protocol for a mixed-methods study.共同设计高危个体肺癌筛查的招募策略:一项混合方法研究的方案
HRB Open Res. 2023 Nov 13;6:64. doi: 10.12688/hrbopenres.13793.1. eCollection 2023.
5
The efficacy of machine learning models in lung cancer risk prediction with explainability.具有可解释性的机器学习模型在肺癌风险预测中的疗效。
PLoS One. 2024 Jun 13;19(6):e0305035. doi: 10.1371/journal.pone.0305035. eCollection 2024.
6
Perceptions and feelings of a French sample regarding lung cancer screening.法国人群对肺癌筛查的认知和感受。
BMC Public Health. 2023 Nov 24;23(1):2333. doi: 10.1186/s12889-023-17110-8.
7
Artificial Intelligence in Lung Cancer Screening: The Future Is Now.人工智能在肺癌筛查中的应用:未来已来。
Cancers (Basel). 2023 Aug 30;15(17):4344. doi: 10.3390/cancers15174344.
8
Invitation strategies and participation in a community-based lung cancer screening programme located in areas of high socioeconomic deprivation.邀请策略和参与位于高社会经济剥夺地区的社区肺癌筛查计划。
Thorax. 2023 Dec 15;79(1):58-67. doi: 10.1136/thorax-2023-220001.
9
Optimising recruitment to a lung cancer screening trial: A comparison of general practitioner and community-based recruitment.优化肺癌筛查试验的招募工作:全科医生与社区招募方式的比较
J Med Screen. 2024 Mar;31(1):46-52. doi: 10.1177/09691413231190785. Epub 2023 Aug 1.
10
Preferences for Decision Control among a High-Risk Cohort Offered Lung Cancer Screening: A Brief Report of Secondary Analyses from the Lung Screen Uptake Trial (LSUT).肺癌筛查高危队列中决策控制的偏好:来自肺癌筛查接受试验(LSUT)二次分析的简要报告
MDM Policy Pract. 2023 Mar 27;8(1):23814683231163190. doi: 10.1177/23814683231163190. eCollection 2023 Jan-Jun.