title: Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study.
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.
  
  
  RESULTS
  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.
authors.affiliation: Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
authors.forename: Kevin
authors.initials: K
authors.lastname: Ten Haaf
authors.affiliation: Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Jihyoun
authors.identifier: http://orcid.org/0000-0001-7003-3412
authors.initials: J
authors.lastname: Jeon
authors.affiliation: Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada.
authors.forename: Martin C
authors.initials: MC
authors.lastname: Tammemägi
authors.affiliation: Department of Radiology, Stanford University, Palo Alto, California, United States of America.|Department of Medicine, Stanford University, Palo Alto, California, United States of America.
authors.forename: Summer S
authors.initials: SS
authors.lastname: Han
authors.affiliation: Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
authors.forename: Chung Yin
authors.initials: CY
authors.lastname: Kong
authors.affiliation: Department of Radiology, Stanford University, Palo Alto, California, United States of America.
authors.forename: Sylvia K
authors.initials: SK
authors.lastname: Plevritis
authors.affiliation: Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, United States of America.
authors.forename: Eric J
authors.identifier: http://orcid.org/0000-0003-4842-7751
authors.initials: EJ
authors.lastname: Feuer
authors.affiliation: Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
authors.forename: Harry J
authors.initials: HJ
authors.lastname: de Koning
authors.affiliation: Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
authors.affiliation: Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Rafael
authors.identifier: http://orcid.org/0000-0002-1076-5037
authors.initials: R
authors.lastname: Meza
mesh_terms: D000368:Aged; D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D008403:Mass Screening / Q000379:methods; D008875:Middle Aged; D008962:Models, Theoretical; D018579:Patient Selection; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors