Alexandris Panos, Quaife Samantha, Berg Christine D, Callister Matthew, Crosbie Philip Aj, Davies Michael Pa, de Koning Harry J, Field John K, Hammer Mark M, Horst Carolyn, Janes Sam, Nair Arjun, Rintoul Robert C, Gabe Rhian, Duffy Stephen
Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
BMJ Open. 2025 Jan 25;15(1):e085118. doi: 10.1136/bmjopen-2024-085118.
Worldwide, lung cancer (LC) is the second most frequent cancer and the leading cause of cancer related mortality. Low-dose CT (LDCT) screening reduced LC mortality by 20-24% in randomised trials of high-risk populations. A significant proportion of those screened have nodules detected that are found to be benign. Consequently, many individuals receive extra imaging and/or unnecessary procedures, which can have a negative physical and psychological impact, as well as placing a financial burden on health systems. Therefore, there is a need to identify individuals who need no interval CT between screening rounds.
The aim of this study is to identify risk factors predictive of LC, which are known at the time of the scan, in patients with LDCT screen-detected lung nodules. The MEDLINE and EMBASE databases will be searched and articles that are on cohorts or mention cohorts of screenees with nodules will be identified. A data extraction framework will ensure consistent extraction across studies. Individual participant data (IPD) will be collected to perform a one-stage IPD meta-analysis using hierarchical univariate models. Clustering will be accounted for by having separate intercept terms for each cohort. Where IPD is not available, the effects of risk factors will be extracted from publications, if possible. Effects from IPD cohorts and aggregate data will be reported and compared. The PROBAST (Prediction model Risk Of Bias ASsessment Tool) will be used for assessment of quality of the studies.
Ethical approval was not required as this study is a secondary analysis. The results will be disseminated through publication in peer-reviewed journals and presentations at relevant conferences.
CRD42022309515.
在全球范围内，肺癌是第二常见的癌症，也是癌症相关死亡的主要原因。在高危人群的随机试验中，低剂量CT（LDCT）筛查使肺癌死亡率降低了20%-24%。很大一部分接受筛查的人检测出有结节，而这些结节后来被发现是良性的。因此，许多人接受了额外的影像学检查和/或不必要的程序，这可能会对身体和心理产生负面影响，同时也给卫生系统带来经济负担。因此，有必要识别出在筛查轮次之间不需要进行间隔CT检查的个体。
本研究的目的是在LDCT筛查发现肺部结节的患者中，识别出扫描时已知的、可预测肺癌的危险因素。将检索MEDLINE和EMBASE数据库，并识别有关队列或提及有结节筛查对象队列的文章。数据提取框架将确保各项研究的提取一致性。将收集个体参与者数据（IPD），使用分层单变量模型进行单阶段IPD荟萃分析。通过为每个队列设置单独的截距项来考虑聚类情况。如果无法获得IPD，则尽可能从出版物中提取危险因素的效应。将报告并比较IPD队列和汇总数据的效应。将使用PROBAST（预测模型偏倚风险评估工具）评估研究质量。
由于本研究是二次分析，无需伦理批准。研究结果将通过在同行评审期刊上发表以及在相关会议上进行报告来传播。
PROSPERO注册号：CRD42022309515。
