肿瘤学大会系列旨在将《临床肿瘤学杂志》发表的原创报告置于临床环境中。在病例陈述之后，描述了诊断和治疗中的挑战，回顾了相关文献，并总结了作者提议的治疗方法。这个系列的目标是帮助读者更好地理解如何将关键研究结果，包括发表在《临床肿瘤学杂志》上的结果，应用于他们自己的临床实践中的患者。肺癌筛查已被证明能减少肺癌的死亡率，但其效益必须与不必要的程序、假阳性影像结果和过度诊断的潜在危害相权衡。肺癌风险最高的个体更有可能从筛查中最大化收益并最小化危害。尽管美国预防服务工作组（USPSTF）推荐的当前肺癌筛查指南仅考虑年龄和吸烟史作为筛查资格，但国家综合癌症网络和其他学会指南建议根据个体化风险评估包括家族史、环境暴露和慢性肺部疾病的存在进行筛查。已开发了风险预测模型，将各种风险因素整合到个性化风险预测评分中。以往的证据表明，基于风险预测模型的筛查资格可以提高肺癌病例的敏感性，而不降低特异性。此外，肺癌生物标志物的最新进展改善了风险预测在识别肺癌病例方面的性能，相对于USPSTF标准。这些风险预测模型可用于在进行肺癌筛查之前引导共同决策讨论。本研究旨在提供对这些预测模型和生物标志物测试在风险预测中新兴角色的简明概述，以促进与患者的对话。旨在协助临床医生评估个体患者风险，从而做出更明智的决策。

The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors' suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology, to patients seen in their own clinical practice.Lung cancer screening has been demonstrated to reduce lung cancer mortality, but its benefits must be weighed against the potential harms of unnecessary procedures, false-positive radiological findings, and overdiagnosis. Individuals at highest risk of lung cancer are more likely to maximize benefits while minimizing harm from screening. Although current lung cancer screening guidelines recommended by the US Preventive Services Task Force (USPSTF) only consider age and smoking history for screening eligibility, National Comprehensive Cancer Network and other society guidelines recommend screening on the basis of individualized risk assessment including family history, environmental exposures, and presence of chronic lung disease. Risk prediction models have been developed to integrate various risk factors into an individualized risk prediction score. Previous evidence showed that risk prediction model-based screening eligibility could improve sensitivity for detecting lung cancer cases without reducing specificity. Furthermore, recent advances in lung cancer biomarkers have enhanced the performance of risk prediction in identifying lung cancer cases relative to the USPSTF criteria. These risk prediction models can be used to guide shared decision-making discussions before proceeding with lung cancer screening. This study aims to provide a concise overview of these prediction models and the emerging role of biomarker testing in risk prediction to facilitate conversations with patients. The goal was to assist clinicians in assessing individual patient risk, leading to more informed decision making.