实施低剂量胸部 CT 进行肺部筛查为通过早期检测和拦截来推进肺癌治疗提供了重要机会。此外，美国每年都会偶然发现数百万个肺结节，这增加了早期肺癌诊断的机会。然而，充分发挥这些机会的潜力取决于准确分析图像数据以进行结节分类和早期肺癌表征的能力。本综述概述了胸部 CT 中使用语义表征的传统图像分析方法，以及使用 CT 衍生的放射组学特征和深度学习架构来表征肺结节和早期癌症的机器学习模型技术和应用的最新进展。目前在将这些决策辅助转化为临床实践时面临的方法学挑战，以及异构成像参数、最佳特征选择、模型选择的技术障碍，以及出于训练和验证目的对注释良好的图像数据集的需求，将进行审查，以期最终将这些潜在的强大决策辅助纳入常规临床实践。© RSNA，2023。

The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.© RSNA, 2023.