Researchers have developed three lung cancer risk prediction models for current, former, and never smokers. Reliable risk prediction models would be of great value for determining an individual's likelihood of developing lung cancer and his or her potential benefit from preventive treatment or clinical trials. However, existing models focus primarily on long-term smokers. Margaret Spitz, M.D., of the University of Texas M.D. Anderson Cancer Center in Houston, and colleagues developed and validated separate risk prediction models for current, former, and never smokers. The models were based on data from a case-control study of lung cancer that included 1,851 lung cancer patients and 2,001 matched control subjects. The models predicted lung cancer development with modest accuracy, similar to that of other cancer prediction models. Risk factors in the models include exposure to second-hand smoke, family history of cancer, dust exposure, prior respiratory disease, and smoking history. "The purpose of this analysis was to create a parsimonious model for assessing lung cancer risk with a minimal number of risk predictors that is realistic to use in clinical practice and to validate the model in an independent sample from the same population. In our experience, patients are agreeable to completing health questionnaires, either self-administered or administered by personal interview," the authors write. Contact: Laura Sussman ### Other highlights in the May 2 JNCI Also in the May 2 JNCI: *

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