Incremental value of pulmonary function and sputum DNA image cytometry in lung cancer risk prediction.

Lung cancer is the leading cause of cancer death worldwide. Accurate prediction of lung cancer risk is of value for individuals, clinicians, and researchers. The aims of this study were to characterize the associations between pulmonary function and sputum DNA image cytometry (SDIC) and lung cancer, and their contributions to risk prediction. During 1990 to 2007, 2,596 high-risk individuals were enrolled and followed prospectively for development of lung cancer (n = 139; median follow-up 7.7 years) in trials at the British Columbia Cancer Agency. At baseline, an epidemiologic questionnaire was administered, sputum was collected for aneuploidy measurement and spirometry was obtained. Multivariable logistic models were prepared including known lung cancer predictors (model 1), that additionally included percent-expected-forced expiratory volume in 1 second [forced expiratory volume in 1 second (FEV(1)%), model 2], and that additionally included SDIC (model 3). Prediction was assessed by evaluating discrimination (receiver operator characteristic area under the curve (ROC AUC)) and calibration. Net reclassification indices (NRI) were calculated with cutoff points for 8-year risks identifying low, intermediate, and high risk at 1.5% and 3%. Lung cancer risk increased with decline in FEV(1)%, but did so more for men than for women (interaction P < 0.001). SDIC demonstrated a dose-response with lung cancer (P = 0.022). The ROC AUCs for models 1, 2, and 3 were 0.718 (95% CI: 0.671-0.765), 0.767 (95% CI: 0.725-0.809), and 0.773 (95% CI: 0.732-0.815), respectively. Model 2 versus 1 had a NRI of 12.6% (P < 0.0001) and model 3 versus 2 had a NRI of 3.1% (P = 0.059). Spirometry and SDIC data substantially and minimally improved lung cancer prediction, respectively.