title: The contribution of risk prediction models to early detection of lung cancer.
abstract: Low-dose computed tomography screening is a strategy for early diagnosis of lung cancer. The success of such screening will be dependent upon identifying populations at sufficient risk in order to maximise the benefit-to-harm ratio of the intervention. To facilitate this, the lung cancer risk prediction community has established several risk models with good predictive performance. This review focuses on current progress in risk modelling for lung cancer prediction, with some views on future development.
authors.affiliation: Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, The University of Liverpool Cancer Research Centre, Liverpool, UK.
authors.forename: John K
authors.initials: JK
authors.lastname: Field
authors.forename: Ying
authors.initials: Y
authors.lastname: Chen
authors.forename: Michael W
authors.initials: MW
authors.lastname: Marcus
authors.forename: Fiona E
authors.initials: FE
authors.lastname: Mcronald
authors.forename: Olaide Y
authors.initials: OY
authors.lastname: Raji
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Duffy
mesh_terms: D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D015233:Models, Statistical; D012306:Risk; D040242:Risk Reduction Behavior; D014057:Tomography, X-Ray Computed
keywords: early diagnosis; lung neoplasms; risk assessment; screening; statistical model