Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial
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Multi-source data approach for personalized outcome prediction in lung cancer screening : update from the NELSON trial. / Sidorenkov, Grigory; Stadhouders, Ralph; Jacobs, Colin; Mohamed Hoesein, Firdaus A.A.; Gietema, Hester A.; Nackaerts, Kristiaan; Saghir, Zaigham; Heuvelmans, Marjolein A.; Donker, Hylke C.; Aerts, Joachim G.; Vermeulen, Roel; Uitterlinden, Andre; Lenters, Virissa; van Rooij, Jeroen; Schaefer-Prokop, Cornelia; Groen, Harry J.M.; de Jong, Pim A.; Cornelissen, Robin; Prokop, Mathias; de Bock, Geertruida H.; Vliegenthart, Rozemarijn.
I: European Journal of Epidemiology, Bind 38, Nr. 4, 2023, s. 445-454.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Multi-source data approach for personalized outcome prediction in lung cancer screening
T2 - update from the NELSON trial
AU - Sidorenkov, Grigory
AU - Stadhouders, Ralph
AU - Jacobs, Colin
AU - Mohamed Hoesein, Firdaus A.A.
AU - Gietema, Hester A.
AU - Nackaerts, Kristiaan
AU - Saghir, Zaigham
AU - Heuvelmans, Marjolein A.
AU - Donker, Hylke C.
AU - Aerts, Joachim G.
AU - Vermeulen, Roel
AU - Uitterlinden, Andre
AU - Lenters, Virissa
AU - van Rooij, Jeroen
AU - Schaefer-Prokop, Cornelia
AU - Groen, Harry J.M.
AU - de Jong, Pim A.
AU - Cornelissen, Robin
AU - Prokop, Mathias
AU - de Bock, Geertruida H.
AU - Vliegenthart, Rozemarijn
N1 - Publisher Copyright: © 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - 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%.
AB - 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%.
KW - CT screening
KW - Imaging biomarkers
KW - Lung cancer
KW - Lung nodules
KW - Prediction model
U2 - 10.1007/s10654-023-00975-9
DO - 10.1007/s10654-023-00975-9
M3 - Journal article
C2 - 36943671
AN - SCOPUS:85150434425
VL - 38
SP - 445
EP - 454
JO - European Journal of Epidemiology
JF - European Journal of Epidemiology
SN - 0393-2990
IS - 4
ER -
ID: 367088334