试验表明，低剂量计算机断层扫描（CT）可以降低长期（前）吸烟者的肺癌死亡率。然而，许多人经历了不必要的诊断过程。该项目旨在通过确定高风险参与者和改进结节的风险鉴别来提高肺癌筛查的效率。本研究是荷兰比利时随机肺癌筛查试验的延续，重点关注个性化预测结果（NELSON-POP）。数据将添加到遗传学、空气污染、肺结节的恶性风险以及肺结节（肺气肿、冠状动脉钙化、骨密度、椎体高度和身体组成）之外的CT生物标志物上。将确定多基因风险评分和空气污染对筛查发现的肺癌诊断和生存的影响。将评估基线和事发筛选轮之间的基于AI的结节恶性度评分与肺癌之间的关联。将建立胸部CT影像生物标志物与结果的联系。根据这些结果，将开发基于多源预测模型的筛选前和筛选后基于基线的参与者选择和结节管理。新模型将进行外部验证。我们的假设是，我们可以确定15-20％的低风险肺癌或寿命预期短的参与者，从而防止将近14万名荷兰人经历不必要的筛查。我们的模型将比仅评估结节大小/增长具有10％的提高特异性，而不会失去敏感性，从而减少40-50％的不必要工作。 © 2023年。作者（们）。

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%.© 2023. The Author(s).