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External validation and recalibration of the Brock model to predict probability of cancer in pulmonary nodules using NLST data
  1. Audrey Winter,
  2. Denise R Aberle,
  3. William Hsu
  1. Department of Radiological Sciences, Medical Imaging Informatics, University of California, Los Angeles, California, USA
  1. Correspondence to Dr Audrey Winter, Department of Radiological Sciences, Medical Imaging Informatics, University of California, Los Angeles, CA 90095, USA; audrey.winter89{at}


Introduction We performed an external validation of the Brock model using the National Lung Screening Trial (NLST) data set, following strict guidelines set forth by the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement. We report how external validation results can be interpreted and highlight the role of recalibration and model updating.

Materials and methods We assessed model discrimination and calibration using the NLST data set. Adhering to the inclusion/exclusion criteria reported by McWilliams et al, we identified 7879 non-calcified nodules discovered at the baseline low-dose CT screen with 2 years of follow-up. We characterised differences between Pan-Canadian Early Detection of Lung Cancer Study and NLST cohorts. We calculated the slope on the prognostic index and the intercept coefficient by fitting the original Brock model to NLST. We also assessed the impact of model recalibration and the addition of new covariates such as body mass index, smoking status, pack-years and asbestos.

Results While the area under the curve (AUC) of the model was good, 0.905 (95% CI 0.882 to 0.928), a histogram plot showed that the model poorly differentiated between benign and malignant cases. The calibration plot showed that the model overestimated the probability of cancer. In recalibrating the model, the coefficients for emphysema, spiculation and nodule count were updated. The updated model had an improved calibration and achieved an optimism-corrected AUC of 0.912 (95% CI 0.891 to 0.932). Only pack-year history was found to be significant (p<0.01) among the new covariates evaluated.

Conclusion While the Brock model achieved a high AUC when validated on the NLST data set, the model benefited from updating and recalibration. Nevertheless, covariates used in the model appear to be insufficient to adequately discriminate malignant cases.

  • lung cancer
  • prediction
  • external validation
  • Brock model
  • recalibration

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  • Correction notice This article has been corrected since it was published. The third sentence in the Introduction section was re-worded.

  • Contributors Substantial contributions to the conception or design of the work, or the acquisition, analysis or interpretation of data: AW, DRA, WH. Drafting the work or revising it critically for important intellectual content: AW, DRA, WH. Final approval of the version published: AW, DRA, WH.

  • Funding Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award number R01CA210360 and by the Department of Radiological Sciences under the Integrated Diagnostics Program.

  • Disclaimer The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement These data are restricted and cannot be publicly available, but permission access can be requested through this website (