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.
KEYWORDS
Risk models - discriminatory power - accuracy - clinical utility - screening - chemoprevention
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Peter B BachM.D.
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center
1275 York Ave., New York, NY 10065
Email: bachp@mskcc.org
