A Risk Model for Prediction of Lung Cancer

作者：M R Spitz, W. K. Hong, Christopher Ian Amos, Xifeng Wu, Matthew B. Schabath, Qian Dong, Sanjay S. Shete, Carol J. Etzel · 发表于：JNCI Journal of the National Cancer Institute · 年份：2007 · DOI：10.1093/jnci/djk153 · 被引用次数：426 · 研究领域：Lung Cancer Diagnosis and Treatment、Lung Cancer Treatments and Mutations、Lung Cancer Research Studies

BACKGROUND: Reliable risk prediction tools for estimating individual probability of lung cancer have important public health implications. We constructed and validated a comprehensive clinical tool for lung cancer risk prediction by smoking status. METHODS: Epidemiologic data from 1851 lung cancer patients and 2001 matched control subjects were randomly divided into separate training (75% of the data) and validation (25% of the data) sets for never, former, and current smokers, and multivariable models were constructed from the training sets. The discriminatory ability of the models was assessed in the validation sets by examining the areas under the receiver operating characteristic curves and with concordance statistics. Absolute 1-year risks of lung cancer were computed using national incidence and mortality data. An ordinal risk index was constructed for each smoking status category by summing the odds ratios from the multivariable regression analyses for each risk factor. RESULTS: All variables that had a statistically significant association with lung cancer (environmental tobacco smoke, family history of cancer, dust exposure, prior respiratory disease, and smoking history variables) have strong biologically plausible etiologic roles in the disease. The concordance statistics in the validation sets for the never, former, and current smoker models were 0.57, 0.63, and 0.58, respectively. The computed 1-year absolute risk of lung cancer for a hypothetical male current sm...