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

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

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

Cited by 90 publications

(66 citation statements)

References 39 publications

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“…Previous studies have also compared the performance of different lung cancer risk prediction models [20,21]. D’Amelio et al examined the discriminatory performance of three risk prediction models for lung cancer incidence in a case–control study and found modest differences between the models [20].…”

Section: Discussionmentioning

confidence: 99%

“…However, this study considered a limited number of participants (1,066 cases and 677 controls) and did not consider other aspects of model performance such as calibration or clinical usefulness. Li et al examined four risk prediction models for lung cancer incidence in German participants of the European Prospective Investigation into Cancer and Nutrition cohort [21]. They found that while the differences between most of the evaluated models were modest, generally only the Bach and the PLCOm2012 models had similar or better sensitivity and specificity compared to the eligibility criteria used in the NLST and other eligibility criteria that were used in various European lung cancer screening trials (which applied less restrictive smoking eligibility criteria than the NLST).…”

Section: Discussionmentioning

confidence: 99%

“…While various lung cancer risk prediction models have been developed, external validation and direct comparisons between models have been limited due to insufficient numbers of events or methodological limitations [15–21]. Such validations are essential, as risk prediction models generally have optimistic performance within their development dataset [15–17].…”

Section: Introductionmentioning

confidence: 99%

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

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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. T...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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“…The LLP model also exhibited modest to good discrimination (AUCs, 0.67–0.82) when assessed alone in several European and U.S. study populations [44]. In more recent and extensive external validation studies, however, the PLCO M2012 model demonstrated the best performance, with respect to discrimination, calibration, sensitivity, and specificity, although not exceedingly better than the Bach model [45, 46]. In support, a study of >95,000 Australian smokers aged ≥45 years also found that the PLCO M2012 model displayed good calibration and discrimination (AUC, 0.80), and that its performance was largely driven by the main predictors of the Bach model, age and smoking history [47].…”

Section: Predicting Lung Cancer Risk Prior To Screening Initiationmentioning

confidence: 99%

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

et al. 2017

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Structured Abstract Purpose of review Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Recent findings Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Summary Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

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“…As in the NLST, the eligibility criteria for the European trials were based on age and a simplified index of past cumulative smoking exposure, plus time since quitting for former smokers. The criteria, however, differed from those in the NLST in terms of specific age range, minimum lifetime smoking duration, cumulative smoking exposure (pack years), and maximum time since smoking cessation (7,8) and the criteria varied also across the European trials themselves.…”

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Selecting high-risk individuals for lung cancer screening; the use of risk prediction models vs. simplified eligibility criteria

Kaaks

Hüsing

Fortner

2017

Annals of Translational Medicine

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showed a 20% reduction in lung-cancer mortality among long-term heavy smokers screened by low-dose spiral computed tomography (LDCT) compared to standard X-ray diagnostics (1), and the US Prevention Services Task Force (USPSTF) recommends lung cancer screening for all individuals who meet the original NLST eligibility criteria of being 55 to 80 years of age, having smoked 30 or more pack years, and not having quit smoking more than 15 years ago. In Europe, among seven European lung cancer screening trials, jointly including less than 38,000 participants (2) vs. more than 53,000 in the NLST alone, preliminary results from four studies so far did not provide definitive confirmation of a mortality reduction (3-5), but individually these trials were small and lacked statistical power. A final evaluation of the effects on lung cancer mortality in at least three further European trials, including the larger Dutch-Belgian NELSON study (n=7,915 in the screening arm) (6), is expected within the next 1-2 years, as well as a pooled analysis of the European trials. As in the NLST, the eligibility criteria for the European trials were based on age and a simplified index of past cumulative smoking exposure, plus time since quitting for former smokers. The criteria, however, differed from those in the NLST in terms of specific age range, minimum lifetime smoking duration, cumulative smoking exposure (pack years), and maximum time since smoking cessation (7,8) and the criteria varied also across the European trials themselves.At start of the trials, a major motivation for focusing screening on individuals meeting these eligibility criteria was cost. The vast majority of lung cancer cases occur among long-term smokers and at a more advanced age, and focusing on high-risk individuals allows drastic reduction in the number of individuals needed to be screened to identify one case, while still capturing the majority of individuals who develop lung cancer. A further motivation for focusing on high-risk individuals is that it enriches the screened population with participants who may have a benefit from screening (i.e., those who will actually have a screen-detectable lung cancer) while avoiding possible harms to individuals who are unlikely to have a lung tumor. The NLST and the various European trials have now provided extensive documentation not only of the potential benefit of screening, in form of mortality reduction and life years that may be gained by early lung cancer detection, but also of the possible harms. One major type of harm is false-positive diagnoses. In NLST and other trials, depending on the diagnostic criteria (e.g., nodule size) and procedures used for further diagnostic work-up, up to 25% of participants were classified as having suspicious nodules necessitating follow-up examinations, associated with psychologic stress and additional radiation exposure during diagnostic verification. Smaller but still significant

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

Cited by 90 publications

References 39 publications

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

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

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

Selecting high-risk individuals for lung cancer screening; the use of risk prediction models vs. simplified eligibility criteria

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