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Original article
Lung cancer risk to personalise annual and biennial follow-up computed tomography screening
  1. Anton Schreuder1,
  2. Cornelia M Schaefer-Prokop1,2,
  3. Ernst T Scholten1,
  4. Colin Jacobs1,
  5. Mathias Prokop1,
  6. Bram van Ginneken1,3
  1. 1 Department of Radiology and Nuclear Medicine, Diagnostic Image Analysis Group, Radboudumc, Nijmegen, The Netherlands
  2. 2 Department of Radiology, Meander Medisch Centrum, Amersfoort, The Netherlands
  3. 3 Fraunhofer MEVIS, Bremen, Germany
  1. Correspondence to Dr Anton Schreuder, Department of Radiology and Nuclear Medicine, Diagnostic Image Analysis Group, Radboudumc, Nijmegen, 6525GA, The Netherlands; antoniusschreuder{at}gmail.com

Abstract

Background All lung cancer CT screening trials used fixed follow-up intervals, which may not be optimal. We developed new lung cancer risk models for personalising screening intervals to 1 year or 2 years, and compared these with existing models.

Methods We included participants in the CT arm of the National Lung Screening Trial (2002–2010) who underwent a baseline scan and a first annual follow-up scan and were not diagnosed with lung cancer in the first year. True and false positives and the area under the curve of each model were calculated. Internal validation was performed using bootstrapping.

Results Data from 24 542 participants were included in the analysis. The accuracy was 0.785, 0.693, 0.697, 0.666 and 0.727 for the polynomial, patient characteristics, diameter, Patz and PanCan models, respectively. Of the 24 542 participants included, 174 (0.71%) were diagnosed with lung cancer between the first and the second annual follow-ups. Using the polynomial model, 2558 (10.4%, 95% CI 10.0% to 10.8%), 7544 (30.7%, 30.2% to 31.3%), 10 947 (44.6%, 44.0% to 45.2%), 16 710 (68.1%, 67.5% to 68.7%) and 20 023 (81.6%, 81.1% to 92.1%) of the 24 368 participants who did not develop lung cancer in the year following the first follow-up screening round could have safely skipped it, at the expense of delayed diagnosis of 0 (0.0%, 0.0% to 2.7%), 8 (4.6%, 2.2% to 9.2%), 17 (9.8%, 6.0% to 15.4%), 44 (25.3%, 19.2% to 32.5%) and 70 (40.2%, 33.0% to 47.9%) of the 174 lung cancers, respectively.

Conclusions The polynomial model, using both patient characteristics and baseline scan morphology, was significantly superior in assigning participants to 1-year or 2-year screening intervals. Implementing personalised follow-up intervals would enable hundreds of participants to skip a screening round per lung cancer diagnosis delayed.

  • lung cancer
  • clinical epidemiology

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Footnotes

  • Contributors Study concept and design: AS, BvG, CMS-P, MP. Acquisition, analysis or interpretation of data: All authors. Drafting of the manuscript: AS. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: AS. Administrative, technical or material support: CJ, ETS. Study supervision: AS, BvG.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests BvG reports other from Thirona, grants from Mevis Medical Solutions, grants from Delft Imaging Systems, all outside the submitted work. CJ reports grants from MeVis Medical Solutions AG, Bremen, Germany, outside the submitted work. MP reports personal fees from Bracco, Bayer, Canon, and Siemens, grants from Canon and Siemens, other from Thirona, all outside the submitted work. AS, CSP, and ETS have nothing to disclose.

  • Patient consent Not required.

  • Ethics approval The institutional review boards of 33 participating medical institutions.

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

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