Research Article: Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study

Date Published: April 4, 2017

Publisher: Public Library of Science

Author(s): Kevin ten Haaf, Jihyoun Jeon, Martin C. Tammemägi, Summer S. Han, Chung Yin Kong, Sylvia K. Plevritis, Eric J. Feuer, Harry J. de Koning, Ewout W. Steyerberg, Rafael Meza, John D Minna

Abstract: BackgroundSelection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation) as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer.Methods and findingsRetrospective analyses were performed on 53,452 National Lung Screening Trial (NLST) participants (1,925 lung cancer cases and 884 lung cancer deaths) and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths). Six-year lung cancer incidence and mortality risk predictions were assessed for (1) calibration (graphically) by comparing the agreement between the predicted and the observed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between individuals with and without lung cancer (death), and (3) clinical usefulness (net benefit in decision curve analysis) by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81). The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher sensitivity for all models and a slightly higher specificity for some models. The PLCOm2012, Bach, and Two-Stage Clonal Expansion incidence models had the best overall performance, with AUCs >0.68 in the NLST and >0.77 in the PLCO. These three models had the highest sensitivity and specificity for predicting 6-y lung cancer incidence in the PLCO chest radiography arm, with sensitivities >79.8% and specificities >62.3%. In contrast, the NLST eligibility criteria yielded a sensitivity of 71.4% and a specificity of 62.2%. Limitations of this study include the lack of identification of optimal risk thresholds, as this requires additional information on the long-term benefits (e.g., life-years gained and mortality reduction) and harms (e.g., overdiagnosis) of risk-based screening strategies using these models. In addition, information on some predictor variables included in the risk prediction models was not available.ConclusionsSelection of individuals for lung cancer screening using individual risk is superior to selection criteria based on age and pack-years alone. The benefits, harms, and feasibility of implementing lung cancer screening policies based on risk prediction models should be assessed and compared with those of current recommendations.

Partial Text: The National Lung Screening Trial (NLST) found that screening with low-dose computed tomography (CT) can reduce lung cancer mortality by 20% [1]. Based on an evidence review, including the results of the NLST and a comparative microsimulation modeling study, the United States Preventive Services Task Force (USPSTF) recommended lung cancer screening for current and former smokers aged 55 through 80 y who smoked at least 30 pack-years and, if quit, quit less than 15 y ago [2–4]. To our knowledge, only the United States has implemented lung cancer screening policies. Although the province of Ontario, Canada, recommends screening individuals at high risk for lung cancer through an organized program, no program has yet been established [5]. Cancer Care Ontario (the provincial cancer agency of Ontario) is currently evaluating the feasibility of implementing such a program [6]. European countries have not yet made any recommendations on lung cancer screening, as the final results of the Dutch-Belgian Lung Cancer Screening Trial (Nederlands-Leuvens Longkanker Screenings Onderzoek [NELSON] trial), potentially pooled with high-quality data from other trials, are still awaited [7–9].

This study assessed the performance of nine lung cancer risk prediction models in two large randomized controlled trials: the NLST and the PLCO. The models had satisfactory calibration, had modest to good discrimination, and provided a substantial range of risk thresholds with a positive net benefit compared with the NLST eligibility criteria. Given appropriate model-specific risk thresholds, all risk prediction models had a better sensitivity and specificity than the NLST eligibility criteria. This implies that lung cancer risk prediction models, when coupled with model-specific risk thresholds, outperform currently recommended lung cancer screening eligibility criteria (Tables 3 and 4; Fig 6).

Source:

http://doi.org/10.1371/journal.pmed.1002277

 

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