Toumazis Iakovos, Bastani Mehrad, Han Summer S, Plevritis Sylvia K
Departments of Biomedical Data Science and of Radiology, Stanford University, Stanford, CA USA.
Quantitative Science Unit, Department of Medicine, Stanford University, Stanford, CA, USA.
Lung Cancer. 2020 Sep;147:154-186. doi: 10.1016/j.lungcan.2020.07.007. Epub 2020 Jul 12.
Lung cancer remains the leading cause of cancer related deaths worldwide. Lung cancer screening using low-dose computed tomography (LDCT) has been shown to reduce lung cancer specific mortality. In 2013, the United States Preventive Services Task Force (USPSTF) recommended annual lung cancer screening with LDCT for smokers aged between 55 years to 80 years, with at least 30 pack-years of smoking exposure that currently smoke or who have quit smoking within 15 years. Risk-based lung cancer screening is an alternative approach that defines screening eligibility based on the personal risk of individuals. Selection of individuals for lung cancer screening based on their personal lung cancer risk has been shown to improve the sensitivity and specificity associated with the eligibility criteria of the screening program as compared to the 2013 USPSTF criteria. Numerous risk prediction models have been developed to estimate the lung cancer risk of individuals incorporating sociodemographic, smoking, and clinical risk factors associated with lung cancer, including age, smoking history, sex, race/ethnicity, personal and family history of cancer, and history of emphysema and chronic obstructive pulmonary disease (COPD), among others. Some risk prediction models include biomarker information, such as germline mutations or protein-based biomarkers as independent risk predictors, in addition to clinical, smoking, and sociodemographic risk factors. While, the majority of lung cancer risk prediction models are suitable for selecting high-risk individuals for lung cancer screening, some risk models have been developed to predict the probability of malignancy of screen-detected solidary pulmonary nodules or to optimize the screening frequency of eligible individuals by incorporating past screening findings. In this systematic review, we provide an overview of existing risk prediction models and their applications to lung cancer screening. We discuss potential strengths and limitations of lung cancer screening using risk prediction models and future research directions.
肺癌仍然是全球癌症相关死亡的主要原因。使用低剂量计算机断层扫描（LDCT）进行肺癌筛查已被证明可降低肺癌特异性死亡率。2013年，美国预防服务工作组（USPSTF）建议对年龄在55岁至80岁之间、吸烟史至少30包年、目前仍在吸烟或在过去15年内戒烟的吸烟者每年进行LDCT肺癌筛查。基于风险的肺癌筛查是一种替代方法，它根据个体的个人风险来确定筛查资格。与2013年USPSTF标准相比，根据个人肺癌风险选择肺癌筛查个体已被证明可提高筛查项目资格标准的敏感性和特异性。已经开发了许多风险预测模型来估计个体的肺癌风险，这些模型纳入了与肺癌相关的社会人口统计学、吸烟和临床风险因素，包括年龄、吸烟史、性别、种族/族裔、个人和家族癌症史以及肺气肿和慢性阻塞性肺疾病（COPD）史等。一些风险预测模型除了临床、吸烟和社会人口统计学风险因素外，还包括生物标志物信息，如种系突变或基于蛋白质的生物标志物作为独立风险预测指标。虽然大多数肺癌风险预测模型适用于选择肺癌筛查的高危个体，但一些风险模型已被开发用于预测筛查发现的实性肺结节的恶性概率，或通过纳入过去的筛查结果来优化符合条件个体的筛查频率。在本系统评价中，我们概述了现有的风险预测模型及其在肺癌筛查中的应用。我们讨论了使用风险预测模型进行肺癌筛查的潜在优势和局限性以及未来的研究方向。
