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Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening

Anton Schreuder, Colin Jacobs, Nikolas Lessmann, Mireille J.M. Broeders, Mario Silva, Ivana Išgum, Pim A. de Jong, Nicola Sverzellati, Mathias Prokop, Ugo Pastorino, Cornelia M. Schaefer-Prokop, Bram van Ginneken
European Respiratory Journal 2021 58: 2003386; DOI: 10.1183/13993003.03386-2020
Anton Schreuder
1Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
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  • ORCID record for Anton Schreuder
  • For correspondence: antoniusschreuder@gmail.com
Colin Jacobs
1Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
2Fraunhofer MEVIS, Bremen, Germany
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Nikolas Lessmann
1Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
3Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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Mireille J.M. Broeders
4Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
5Dutch Expert Centre for Screening, Nijmegen, The Netherlands
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Mario Silva
6Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
7Section of Radiology, Unit of Surgical Sciences, Dept of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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Ivana Išgum
3Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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Pim A. de Jong
8Dept of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
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Nicola Sverzellati
7Section of Radiology, Unit of Surgical Sciences, Dept of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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Mathias Prokop
1Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
4Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
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Ugo Pastorino
6Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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Cornelia M. Schaefer-Prokop
1Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
9Dept of Radiology, Meander Medisch Centrum, Amersfoort, The Netherlands
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Bram van Ginneken
1Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
2Fraunhofer MEVIS, Bremen, Germany
4Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
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Abstract

Objectives Combined assessment of cardiovascular disease (CVD), COPD and lung cancer may improve the effectiveness of lung cancer screening in smokers. The aims were to derive and assess risk models for predicting lung cancer incidence, CVD mortality and COPD mortality by combining quantitative computed tomography (CT) measures from each disease, and to quantify the added predictive benefit of self-reported patient characteristics given the availability of a CT scan.

Methods A survey model (patient characteristics only), CT model (CT information only) and final model (all variables) were derived for each outcome using parsimonious Cox regression on a sample from the National Lung Screening Trial (n=15 000). Validation was performed using Multicentric Italian Lung Detection data (n=2287). Time-dependent measures of model discrimination and calibration are reported.

Results Age, mean lung density, emphysema score, bronchial wall thickness and aorta calcium volume are variables that contributed to all final models. Nodule features were crucial for lung cancer incidence predictions but did not contribute to CVD and COPD mortality prediction. In the derivation cohort, the lung cancer incidence CT model had a 5-year area under the receiver operating characteristic curve of 82.5% (95% CI 80.9–84.0%), significantly inferior to that of the final model (84.0%, 82.6–85.5%). However, the addition of patient characteristics did not improve the lung cancer incidence model performance in the validation cohort (CT model 80.1%, 74.2–86.0%; final model 79.9%, 73.9–85.8%). Similarly, the final CVD mortality model outperformed the other two models in the derivation cohort (survey model 74.9%, 72.7–77.1%; CT model 76.3%, 74.1–78.5%; final model 79.1%, 77.0–81.2%), but not the validation cohort (survey model 74.8%, 62.2–87.5%; CT model 72.1%, 61.1–83.2%; final model 72.2%, 60.4–84.0%). Combining patient characteristics and CT measures provided the largest increase in accuracy for the COPD mortality final model (92.3%, 90.1–94.5%) compared to either other model individually (survey model 87.5%, 84.3–90.6%; CT model 87.9%, 84.8–91.0%), but no external validation was performed due to a very low event frequency.

Conclusions CT measures of CVD and COPD provides small but reproducible improvements to nodule-based lung cancer risk prediction accuracy from 3 years onwards. Self-reported patient characteristics may not be of added predictive value when CT information is available.

Abstract

Quantitative computed tomography measures of cardiovascular disease and COPD may provide small but reproducible improvements to lung cancer risk prediction accuracy in a screening setting https://bit.ly/3sWyUMM

Footnotes

  • This article has supplementary material available from erj.ersjournals.com

  • Author contributions: A. Schreuder: conceived and designed the analysis, performed the analysis, wrote the paper. C. Jacobs: conceived the analysis, supervised the analysis, collected the data, contributed data and analysis tools, critically appraised the paper. N. Lessmann: conceived the analysis, collected the data, contributed data and analysis tools, critically appraised the paper. M.J.M. Broeders: conceived the analysis, supervised the analysis, critically appraised the paper. M. Silva: conceived the analysis, collected the data, contributed data, critically appraised the paper. I. Išgum: conceived the analysis, contributed analysis tools, critically appraised the paper. P.A. de Jong: conceived the analysis, contributed analysis tools, critically appraised the paper. N. Sverzellati: conceived the analysis, collected the data, contributed data, critically appraised the paper. M. Prokop: conceived the analysis, critically appraised the paper. U. Pastorino: conceived the analysis, collected the data, contributed data, critically appraised the paper. C.M. Schaefer-Prokop: conceived the analysis, critically appraised the paper. B. van Ginneken: conceived the analysis, supervised the analysis, contributed analysis tools, critically appraised the paper.

  • Conflict of interest: A. Schreuder has nothing to disclose.

  • Conflict of interest: C. Jacobs reports grants from MeVis Medical Solutions AG, Bremen, Germany, outside the submitted work.

  • Conflict of interest: N. Lessmann has nothing to disclose.

  • Conflict of interest: M.J.M. Broeders has nothing to disclose.

  • Conflict of interest: M. Silva has nothing to disclose.

  • Conflict of interest: I. Išgum has nothing to disclose.

  • Conflict of interest: P.A. de Jong reports other (research support to institution) from Philips Healthcare, during the conduct of the study.

  • Conflict of interest: N. Sverzellati has nothing to disclose.

  • Conflict of interest: M. Prokop reports personal fees for lectures from Bracco, Bayer, Toshiba and Siemens, grants from Toshiba, other (department spin-off) from Thiroux, outside the submitted work.

  • Conflict of interest: U. Pastorino has nothing to disclose.

  • Conflict of interest: C.M. Schaefer-Prokop has nothing to disclose.

  • Conflict of interest: B. van Ginneken reports other (co-founder/shareholder) from Thirona, grants/royalties from Mevis Medical Solutions and Delft Imaging Systems, outside the submitted work.

  • Received September 7, 2020.
  • Accepted January 18, 2021.
  • Copyright ©The authors 2021. For reproduction rights and permissions contact permissions{at}ersnet.org
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Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening
Anton Schreuder, Colin Jacobs, Nikolas Lessmann, Mireille J.M. Broeders, Mario Silva, Ivana Išgum, Pim A. de Jong, Nicola Sverzellati, Mathias Prokop, Ugo Pastorino, Cornelia M. Schaefer-Prokop, Bram van Ginneken
European Respiratory Journal Sep 2021, 58 (3) 2003386; DOI: 10.1183/13993003.03386-2020

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Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening
Anton Schreuder, Colin Jacobs, Nikolas Lessmann, Mireille J.M. Broeders, Mario Silva, Ivana Išgum, Pim A. de Jong, Nicola Sverzellati, Mathias Prokop, Ugo Pastorino, Cornelia M. Schaefer-Prokop, Bram van Ginneken
European Respiratory Journal Sep 2021, 58 (3) 2003386; DOI: 10.1183/13993003.03386-2020
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