随着低剂量螺旋CT（low-dose computed tomography，LDCT）筛查的推广及胸部CT在体检、慢病管理和多学科诊疗中的广泛应用，肺结节检出率持续升高。肺结节管理的重点亦由“发现病灶”逐步转向“精细评估、风险分层和动态随访”。传统影像判读主要依赖结节直径、密度、边缘及增长趋势等指标，虽在临床实践中具有重要价值，但在小结节、血管旁结节、贴胸膜结节、亚实性结节及多发结节等复杂场景中，仍存在评估者差异大、重复性不足和纵向比较困难等问题。深度学习能够直接从二维、三维乃至多时点CT影像中自动提取多尺度表征特征，已被广泛应用于肺结节自动检出、精准分割、表型刻画、良恶性风险评估、动态随访及进展预测等多个环节。本文围绕肺结节影像精细评估的临床基础与管理框架，系统综述深度学习在肺结节检出与分割、影像表型精细刻画、良恶性分层决策、时间序列随访分析及临床转化方面的研究进展，并结合Fleischner学会指南、ACR Lung-RADS、BTS指南及近年亚实性结节相关共识，讨论深度学习在真实世界应用中的主要瓶颈，包括外部验证不足、标注真值异质性、可解释性有限、概率校准不足及流程整合困难等。

With the increasing adoption of low-dose computed tomography (LDCT) screening and the widespread use of chest CT in health examinations, chronic disease management, and multidisciplinary care, the detection rate of pulmonary nodules has risen substantially. Accordingly, the focus of pulmonary nodule management has shifted from simple lesion detection to refined evaluation, risk stratification, and longitudinal follow-up. Traditional radiologic assessment mainly relies on nodule diameter, density, margin characteristics, and interval growth. Although these approaches remain clinically valuable, they are limited by interobserver variability, suboptimal reproducibility, and difficulty in longitudinal comparison, especially in small nodules, juxta-vascular nodules, pleural-based nodules, subsolid nodules, and multiple nodules. Deep learning can automatically extract multi-scale imaging representations from two-dimensional, three-dimensional, and longitudinal CT data. In recent years, it has been widely applied to pulmonary nodule detection, precise segmentation, phenotypic characterization, malignancy risk prediction, dynamic follow-up, and progression forecasting. This review summarizes the clinical basis of refined pulmonary nodule imaging assessment and the current management framework, and systematically discusses recent advances in deep learning for nodule detection and segmentation, radiologic phenotype characterization, malignancy risk stratification, longitudinal follow-up, and clinical translation. In addition, based on the Fleischner Society guidelines, ACR Lung-RADS, BTS guideline, and recent consensus statements on subsolid nodules, this review analyzes the major barriers to real-world implementation, including insufficient external validation, heterogeneity of reference standards, limited interpretability, poor probability calibration, and incomplete workflow integration.