Author
Listed:
- 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
Abstract
Background: Selection 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 findings: Retrospective 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. Conclusions: Selection 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. Kevin ten Haaf and colleagues present a new prediction model for candidate screening for lung cancer. The new model considers an individual's risk rather than age and pack years smoked.Why was this study done?: What did the researchers do and find?: What do these findings mean?:
Suggested Citation
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, 2017.
"Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study,"
PLOS Medicine, Public Library of Science, vol. 14(4), pages 1-24, April.
Handle:
RePEc:plo:pmed00:1002277
DOI: 10.1371/journal.pmed.1002277
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Cited by:
- Maaike Buskermolen & Andrea Gini & Steffie K. Naber & Esther Toes-Zoutendijk & Harry J. de Koning & Iris Lansdorp-Vogelaar, 2018.
"Modeling in Colorectal Cancer Screening: Assessing External and Predictive Validity of MISCAN-Colon Microsimulation Model Using NORCCAP Trial Results,"
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- Fuxiao Li & Xiang Li & Chuanhai Guo & Ruiping Xu & Fenglei Li & Yaqi Pan & Mengfei Liu & Zhen Liu & Chao Shi & Hui Wang & Minmin Wang & Hongrui Tian & Fangfang Liu & Ying Liu & Jingjing Li & Hong Cai , 2019.
"Estimation of Cost for Endoscopic Screening for Esophageal Cancer in a High-Risk Population in Rural China: Results from a Population-Level Randomized Controlled Trial,"
PharmacoEconomics, Springer, vol. 37(6), pages 819-827, June.
- Marcela Fu & Noémie Travier & Juan Carlos Martín-Sánchez & Jose M Martínez-Sánchez & Carmen Vidal & Montse Garcia & on behalf of the LUCAPREV research group, 2018.
"Identifying high-risk individuals for lung cancer screening: Going beyond NLST criteria,"
PLOS ONE, Public Library of Science, vol. 13(4), pages 1-11, April.
- Steven B. Markowitz, 2022.
"Lung Cancer Screening in Asbestos-Exposed Populations,"
IJERPH, MDPI, vol. 19(5), pages 1-15, February.
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