Estimating Individual Risk for Lung Cancer

Carol J. Etzel, Ph.D.; Peter B. Bach, M.D., M.A.P.P.


Semin Respir Crit Care Med. 2011;32(1):3-9. 

In This Article

Abstract and Introduction


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.


Lung cancer accounts for 29% of all male cancer deaths and 26% of all female cancer deaths in the United States.[1,2] In 2010, it is estimated that in the United States there will be 222,520 new cases of lung cancer and 157,300 deaths resulting from lung cancer.[2] Despite advances in the detection and treatment of lung cancer, the overall 5-year survival rate still remains grim: 16% for all stages combined.[2] Reliable risk prediction tools for estimating the probability of lung cancer among smokers could eventually serve a public health need.

Identification of high-risk individuals who would be most likely to benefit from a proven screening or surveillance strategy, or alternatively from chemoprevention, could ultimately help reduce the burden of lung cancer. Such risk stratification would be particularly important if either the screening regimen or the chemopreventive agent confers sizable risks or carries large costs. Under such a scenario, selecting higher-risk individuals will help make these interventions both more efficient and more attractive from a risk-benefit and cost-effectiveness perspective. Risk prediction can be useful in designing large-scale trials of screening and prevention as well, in that selecting high-risk individuals enriches the number of events per subject enrolled.


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