Prediction of risk of lung cancer in populations and in pulmonary nodules: Significant progress to drive changes in paradigms

The ability to estimate the risk of lung cancer is important in three common clinical scenarios: the management of pulmonary nodules, the selection of people for screening with computed tomography and in the early identification of symptomatic disease. The risk prediction models that have been developed have similar themes owing to the strongest risk factors dominating the model. In the management of pulmonary nodules, there is a need to ensure that models reliably predict the chance of malignancy by performing validation studies in the population in which the models will be used. Two models stand out as the better ones in validation studies, one best used for smaller nodules and the other for larger ones. To maximise the cost effectiveness of screening with computed tomography, it is essential to select a population at high enough risk. A number of risk models have been developed, of varying complexity. Simpler models may be easier to use in practice but may miss a minority at high risk who have less common but important risk factors. Identification of early symptomatic lung cancer is important to improve early survival and reduce emergency presentations but single symptoms are non-specific. Risk prediction can improve the targeting of investigation and potentially identify patients early. Clinicians need to embrace the concept of estimating the risk of lung cancer in these three important areas because the evidence is strong enough to support a change in the clinical paradigm. (C) 2015 Elsevier Ireland Ltd. All rights reserved.