European Radiology

, Volume 25, Issue 10, pp 3093–3099 | Cite as

Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial

  • Mathilde M. Winkler Wille
  • Sarah J. van Riel
  • Zaigham Saghir
  • Asger Dirksen
  • Jesper Holst Pedersen
  • Colin Jacobs
  • Laura Hohwü Thomsen
  • Ernst Th. Scholten
  • Lene T. Skovgaard
  • Bram van Ginneken
Chest

Abstract

Objectives

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.

Key points

• 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.

Keywords

Lung cancer screening Diagnostic imaging Computed tomography Risk Solitary pulmonary nodules 

Abbreviations

AUC

Area under the curve

BCCA

British Columbia Cancer Agency

CT

Computed tomography

DLCST

Danish Lung Cancer Screening Trial

NLST

National Lung Screening Trial

OR

Odds ratio

PanCan

Pan-Canadian Early Detection of Lung Cancer Study

ROC

Receiver operating characteristics

SD

Standard deviation

Notes

Acknowledgments

The scientific guarantor of this publication is Asger Dirksen. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. Bram Van Ginneken works for Fraunhofer MEVIS in Bremen, Germany. This study has received funding by The Danish Ministry of Health and AstraZeneca. One of the authors has significant statistical expertise. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Some study subjects have been previously reported in DLCST studies regarding lung cancer screening, lung function, lung density, airway segmentation, and visual assessment of emphysema, airway abnormalities and interstitial abnormalities in the Danish Lung Cancer Screening Trial. However, analyses of this study are new and have not been published before. All relevant references are disclosed. Methodology: prospective, diagnostic study, performed at two institutions.

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Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Mathilde M. Winkler Wille
    • 1
  • Sarah J. van Riel
    • 2
  • Zaigham Saghir
    • 3
  • Asger Dirksen
    • 1
  • Jesper Holst Pedersen
    • 4
  • Colin Jacobs
    • 2
  • Laura Hohwü Thomsen
    • 5
  • Ernst Th. Scholten
    • 2
  • Lene T. Skovgaard
    • 6
  • Bram van Ginneken
    • 2
  1. 1.Department of Respiratory MedicineGentofte HospitalHellerupDenmark
  2. 2.Department of Radiology and Nuclear MedicineRadboud University Medical CenterNijmegenThe Netherlands
  3. 3.Department of Respiratory MedicineHerlev HospitalHerlevDenmark
  4. 4.Department of Thoracic Surgery, RigshospitaletCopenhagen University HospitalKøbenhavn ØDenmark
  5. 5.Department of Respiratory MedicineHvidovre HospitalHvidovreDenmark
  6. 6.Department of BiostatisticsUniversity of CopenhagenKøbenhavn ØDenmark

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