Mortality prediction using a combination of quantitive CT image measures
Principal Investigator
Name
Anton Schreuder
Degrees
M.D.
Institution
Radboudumc
Position Title
Ph.D. student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-437
Initial CDAS Request Approval
Sep 5, 2018
Title
Mortality prediction using a combination of quantitive CT image measures
Summary
This project will use NLST scans and patient information to develop mortality prediction models in a lung cancer screening setting. Quantitative measures of diseases such as COPD and CVD will be extracted from the CT images to include in the models. This project may be considered a follow-up of project ID NLST-267, which resulted in a Thorax publication: https://biometry.nci.nih.gov/cdas/publications/710/
Aims
- To create an improved mortality prediction tool for a lung screening setting.
- To validate the predictive value of quantitative measures of chest CT images.
- To improve the understanding of the link between COPD, CVD, and lung cancer.
Collaborators
Bram van Ginneken, Radboudumc
Colin Jacobs, Radboudumc
Cornelia Schaefer-Prokop, Radboudumc
Mathias Prokop, Radboudumc
Ernst Scholten, Radboudumc
Joep Kamps, Radboudumc
Anton Schreuder, Radboudumc
Ivana Isgum, University Medical Center Utrecht
Nikolas Lessmann, University Medical Center Utrecht
Related Publications
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Scan-based competing death risk model for re-evaluating lung cancer computed tomography screening eligibility.
Schreuder A, Jacobs C, Lessmann N, Broeders MJM, Silva M, Išgum I, de Jong PA, van den Heuvel MM, Sverzellati N, Prokop M, Pastorino U, Schaefer-Prokop CM, van Ginneken B
Eur Respir J. 2021 Oct 14 PUBMED -
Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening.
Schreuder A, Jacobs C, Lessmann N, Broeders MJM, Silva M, Išgum I, de Jong PA, Sverzellati N, Prokop M, Pastorino U, Schaefer-Prokop CM, van Ginneken B
Eur Respir J. 2021 Feb 11 PUBMED