Lung Cancer Risk Prediction Models for Asian Ever-Smokers

Abstract

Introduction: Although lung cancer prediction models are widely used to support risk-based screening, their performance outside Western populations remains uncertain. This study aims to evaluate the performance of 11 existing risk prediction models in multiple Asian populations and to refit prediction models for Asians.

Methods: In a pooled analysis of 186,458 Asian ever-smokers from 19 prospective cohorts, we assessed calibration (expected-to-observed ratio) and discrimination (area under the receiver operating characteristic curve [AUC]) for each model. In addition, we developed the "Shanghai models" to better refine risk models for Asians on the basis of two well-characterized population-based prospective cohorts and externally validated them in other Asian cohorts.

Results: Among the 11 models, the Lung Cancer Death Risk Assessment Tool yielded the highest AUC (AUC [95% confidence interval (CI)] = 0.71 [0.67-0.74] for lung cancer death and 0.69 [0.67-0.72] for lung cancer incidence) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model had good calibration overall (expected-to-observed ratio [95% CI] = 1.06 [0.90-1.25]). Nevertheless, these models substantially underestimated lung cancer risk among Asians who reported less than 10 smoking pack-years or stopped smoking more than or equal to 20 years ago. The Shanghai models were found to have marginal improvement overall in discrimination (AUC [95% CI] = 0.72 [0.69-0.74] for lung cancer death and 0.70 [0.67-0.72] for lung cancer incidence) but consistently outperformed the selected Western models among low-intensity smokers and long-term quitters.

Conclusions: The Shanghai models had comparable performance overall to the best existing models, but they improved much in predicting the lung cancer risk of low-intensity smokers and long-term quitters in Asia.

Keywords: Asia; Calibration; Cohort; Discrimination; Lung cancer; Risk prediction model.

Figure 1.. Calibration and Discrimination of Western Lung Cancer Risk Models in Asian Populations

Abbreviations: AUC-ROC curve, area under the receiver operating characteristic curve; Bach, Bach Model; Hoggart, the Hoggart Model; LCDRAT, Lung Cancer Death Risk Assessment Tool; LCRAT, Lung Cancer Risk Assessment Tool; LLP, Liverpool Lung Project Risk Model; LLPi, Liverpool Lung Project Incidence Risk Model; Pittsburgh, Pittsburgh Predictor; PLCOm2012, Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012; Spitz, Spitz Model. Expected-observed Ratios less than 1 indicate underestimation of the risk and those greater than 1 indicate overestimation of the risk. The AUC value of 0.50 indicates no discrimination (equivalent to random selection). Error bars represent 95% confidence intervals.