Protocol for a systematic review and individual participant data meta-analysis for risk factors for lung cancer in individuals with lung nodules identified by low-dose CT screening
Matthew Callister,
Harry J de Koning,
Robert C Rintoul,
Carolyn Horst,
Arjun Nair,
John K Field,
Christine D Berg,
Samantha Quaife,
Rhian Gabe,
Stephen Duffy,
Sam Janes,
Philip AJ Crosbie,
Mark M Hammer,
Panos Alexandris,
Michael PA Davies
Affiliations
Matthew Callister
Leeds Teaching Hospitals NHS Trust, Leeds, UK
Harry J de Koning
Public Health, Erasmus MC, Rotterdam, The Netherlands
Robert C Rintoul
6 Department of Oncology, University of Cambridge, Cambridge, UK
Carolyn Horst
Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
Arjun Nair
University College Hospital, London, UK
John K Field
1 Molecular and Clinical Cancer Medicine, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
Christine D Berg
4 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
Samantha Quaife
Wolfson Institute of Population Health, Queen Mary University of London, London, UK
Rhian Gabe
Centre for Evaluation and Methods, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
Stephen Duffy
CRUK Department of EMS, Woflson Institute of Preventive Medicine, London, UK
Sam Janes
University College London, London, UK
Philip AJ Crosbie
Division of Infection, Immunity and Respiratory Medicine, The University of Manchester, Manchester, UK
Mark M Hammer
1Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
Panos Alexandris
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
Michael PA Davies
Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, UK
Background 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.Methods and analysis 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.Ethics and dissemination 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.PROSPERO registration number CRD42022309515
