Selecting High-Risk Individuals for Lung Cancer Screening: A Prospective Evaluation of Existing Risk Models and Eligibility Criteria in the German EPIC Cohort.

Kuanrong Li,Disorn Sookthai,Heiner Boeing,Nikolaus Becker,Manuela Bergmann,Anika Hüsing,Rudolf Kaaks

doi:10.1158/1940-6207.capr-14-0424

Abstract

Lung cancer risk prediction models are considered more accurate than the eligibility criteria based on age and smoking in identification of high-risk individuals for screening. We externally validated four lung cancer risk prediction models (Bach, Spitz, LLP, and PLCO(M2012)) among 20,700 ever smokers in the EPIC-Germany cohort. High-risk subjects were identified using the eligibility criteria applied in clinical trials (NELSON/LUSI, DLCST, ITALUNG, DANTE, and NLST) and the four risk prediction models. Sensitivity, specificity, and positive predictive value (PPV) were calculated based on the lung cancers diagnosed in the first 5 years of follow-up. Decision curve analysis was performed to compare net benefits. The number of high-risk subjects identified by the eligibility criteria ranged from 3,409 (NELSON/LUSI) to 1,458 (NLST). Among the eligibility criteria, the DLCST produced the highest sensitivity (64.13%), whereas the NLST produced the highest specificity (93.13%) and PPV (2.88%). The PLCO(M2012) model showed the best performance in external validation (C-index: 0.81; 95% CI, 0.76-0.86; E/O: 1.03; 95% CI, 0.87-1.23) and the highest sensitivity, specificity, and PPV, but the superiority over the Bach model and the LLP model was modest. All the models but the Spitz model showed greater net benefit over the full range of risk estimates than the eligibility criteria. We concluded that all of the lung cancer risk prediction models apart from the Spitz model have a similar accuracy to identify high-risk individuals for screening, but in general outperform the eligibility criteria used in the screening trials.

Highlights

The avoidance of smoking is the obvious primary strategy to prevent lung cancer, for those who already have accumulated a long-term smoking history, or who are at increased risk due to occupational exposures, early detection may be a promising secondary strategy for reducing lung cancerrelated mortality.In 2011, the U.S National Lung Screening Trial (NLST)—the first completed and large-scale randomized trial to examine the efficacy of lung cancer screening by low-dose computed tomography (LDCT)—showed a 20% reduction in lung cancer mortality among long-term heavy smokers screened with LDCT, compared with screening with standard radiography [1]
It is worth noting that for the well-calibrated models, the maximum 5-year absolute risk estimate in our cohort was about 7%. In this German study population, we found that the eligibility criteria used in various lung cancer screening trials and lung cancer risk prediction models overall had broadly similar accuracy in identifying high-risk populations for screening, some performed marginally better than the others
Decision curve analyses indicated that well-validated lung cancer risk prediction models may have broader clinical utility than the binary classifiers, given the generally more comprehensive ranges of risk thresholds for which these models showed a positive net benefit

Summary

Introduction

The avoidance of smoking is the obvious primary strategy to prevent lung cancer, for those who already have accumulated a long-term smoking history, or who are at increased risk due to occupational (e.g. asbestos) exposures, early detection may be a promising secondary strategy for reducing lung cancerrelated mortality.In 2011, the U.S National Lung Screening Trial (NLST)—the first completed and large-scale randomized trial to examine the efficacy of lung cancer screening by low-dose computed tomography (LDCT)—showed a 20% reduction in lung cancer mortality among long-term heavy smokers screened with LDCT, compared with screening with standard radiography [1]. The avoidance of smoking is the obvious primary strategy to prevent lung cancer, for those who already have accumulated a long-term smoking history, or who are at increased risk due to occupational (e.g. asbestos) exposures, early detection may be a promising secondary strategy for reducing lung cancerrelated mortality. Mostly false-positive findings suggestive of cancer in around 25% of participants in each screening round, necessitating follow-up examinations associated with additional radiation exposure and psychologic stress. A smaller proportion of screening participants will eventually be classified with a false-positive disease diagnosis after invasive follow-up by endoscopic and/or surgical examinations. An overall analysis of the screening effect on lung cancer mortality in the European screening trials is still pending, preliminary results from the European studies confirm the high rates of aberrant findings after screening and of false-positive findings after subsequent diagnostic work-up [3,4,5,6, 8]

Methods

Results

Conclusion