title: Estimating individual risk for lung cancer.
abstract: Lung cancer risk prediction models hold the promise of improving patient care and streamlining research. The ultimate goal of these models is to inform clinicians as to which interventions their individual patients should receive to reduce lung cancer-associated morbidity and mortality. In this paper, we discuss the history and current state of lung cancer prediction models, focusing on three models: the Bach model, the Spitz model, and the Liverpool Lung Project (LLP) model. We also discuss the prospects for further development of improved prediction models for lung cancer risk. Although current models can identify those smokers at highest risk for lung cancer, these models are presently of limited use in the clinical setting. Nevertheless, lung cancer risk prediction models can be used during study enrollment to select more appropriate study subjects, and may eventually be useful in identifying patients for lung cancer screening or to receive chemoprevention.
authors.affiliation: Department of Epidemiology, UT MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Carol J
authors.initials: CJ
authors.lastname: Etzel
authors.forename: Peter B
authors.initials: PB
authors.lastname: Bach
mesh_terms: D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology / Q000209:etiology*; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012307:Risk Factors; D012907:Smoking / Q000009:adverse effects*