title: Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial.
abstract: OBJECTIVE
  Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models.
  
  
  METHODS
  From the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate risk discrimination.
  
  
  RESULTS
  AUCs of 0.826-0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST.
  
  
  CONCLUSIONS
  High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor.
  
  
  CONCLUSIONS
  • High accuracy in logistic modelling for lung cancer risk stratification of nodules. • Lung cancer risk prediction is primarily based on size of pulmonary nodules. • Nodule spiculation, age and family history of lung cancer are significant predictors. • Sex does not appear to be a useful risk predictor.
authors.affiliation: Department of Respiratory Medicine, Gentofte Hospital, Kildegårdsvej 28, Opg.1D, st.th., DK-2900, Hellerup, Denmark, mathilde.winkler@gmail.com.
authors.forename: Mathilde M
authors.initials: MM
authors.lastname: Winkler Wille
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: van Riel
authors.forename: Zaigham
authors.initials: Z
authors.lastname: Saghir
authors.forename: Asger
authors.initials: A
authors.lastname: Dirksen
authors.forename: Jesper Holst
authors.initials: JH
authors.lastname: Pedersen
authors.forename: Colin
authors.initials: C
authors.lastname: Jacobs
authors.forename: Laura Hohwü
authors.initials: LH
authors.lastname: Thomsen
authors.forename: Ernst Th
authors.initials: ET
authors.lastname: Scholten
authors.forename: Lene T
authors.initials: LT
authors.lastname: Skovgaard
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
mesh_terms: D000368:Aged; D055088:Early Detection of Cancer; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000517:prevention & control; D008297:Male; D008875:Middle Aged; D055613:Multiple Pulmonary Nodules / Q000000981:diagnostic imaging* / Q000473:pathology; D014057:Tomography, X-Ray Computed