Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial

Grigory Sidorenkov, Ralph Stadhouders, Colin Jacobs, Firdaus A.A. Mohamed Hoesein, Hester A. Gietema, Kristiaan Nackaerts, Zaigham Saghir, Marjolein A. Heuvelmans, Hylke C. Donker, Joachim G. Aerts, Roel Vermeulen, Andre Uitterlinden, Virissa Lenters, Jeroen van Rooij, Cornelia Schaefer-Prokop, Harry J.M. Groen, Pim A. de Jong, Robin Cornelissen, Mathias Prokop, Geertruida H. de BockRozemarijn Vliegenthart*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
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Abstract

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15–20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40–50%.

Original languageEnglish
Pages (from-to)445-454
Number of pages10
JournalEuropean Journal of Epidemiology
Volume38
Issue number4
Early online date21 Mar 2023
DOIs
Publication statusPublished - Apr 2023

Bibliographical note

Funding Information:
This work is supported by funding from the Dutch Cancer Society, Siemens Healthineers, and by the Ministry of Economic Affairs and Climate Policy by means of the Public‐Private Partnerships Allowance made available by the Top Sector Life Sciences & Health to stimulate public–private partnerships.

Publisher Copyright:
© 2023, The Author(s).

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