Lung cancer screening with computerised tomography holds promise, but optimising the balance of benefits and harms via selection of a high risk population is critical. PLCOm2012 is a logistic regression model based on U.S. data, incorporating sociodemographic and health factors, which predicts 6-year lung cancer risk among ever-smokers, and thus may better predict those who might benefit from screening than criteria based solely on age and smoking history. We aimed to validate the performance of PLCOm2012 in predicting lung cancer outcomes in a cohort of Australian smokers. Predicted risk of lung cancer was calculated using PLCOm2012 applied to baseline data from 95,882 ever-smokers aged ≥45 years in the 45 and Up Study (2006-2009). Predictions were compared to lung cancer outcomes captured to June 2014 via linkage to population-wide health databases; a total of 1,035 subsequent lung cancer diagnoses were identified. PLCOm2012 had good discrimination (area under the receiver-operating-characteristic-curve; AUC 0.80, 95%CI 0.78-0.81) and excellent calibration (mean and 90th percentiles of absolute risk difference between observed and predicted outcomes: 0.006 and 0.016, respectively). Sensitivity (69.4%, 95%CI, 65.6-73.0%) of the PLCOm2012 criteria in the 55-74 year age group for predicting lung cancers was greater than that using criteria based on ≥30 pack-years smoking and ≤15 years quit (57.3%, 53.3-61.3%; p < 0.0001), but specificity was lower (72.0%, 71.7-72.4% versus 75.2%, 74.8-75.6%, respectively; p < 0.0001). Targeting high risk people for lung cancer screening using PLCOm2012 might improve the balance of benefits versus harms, and cost-effectiveness of lung cancer screening.
Keywords: low dose computed tomography; lung cancer; mass screening; risk prediction model.
© 2017 UICC.