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

• Published in: European journal of epidemiology Vol. 38; no. 4; pp. 445 - 454
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-04-2023; Springer Nature B.V
• Subjects: Air pollution; Biomarkers; Body composition; Bone composition; Bone density; Calcification; Calcification (ectopic); Cancer screening; Cardiology; Cohort Update; Computed tomography; Customization; Early Detection of Cancer - methods; Emphysema; Epidemiology; Genetics; Humans; Infectious Diseases; Life expectancy; Life span; Lung; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - genetics; Lung nodules; Malignancy; Mass Screening - methods; Medical screening; Medicine; Medicine & Public Health; Multiple Pulmonary Nodules - pathology; Nodules; Oncology; Pollution detection; Prediction models; Prognosis; Public Health; Risk; Smoking; Vertebrae; CT screening; Imaging biomarkers; Lung nodules; Lung cancer; Prediction model
• ISSN: 0393-2990; 1573-7284
• DOI: 10.1007/s10654-023-00975-9

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%.