Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients’ outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called “virtual biopsy”. This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.
肺癌在所有恶性肿瘤中发病率和致死率均居高位。多数肺癌确诊时已属中晚期，此时治疗手段有限，患者生存率较低。肺癌筛查的核心目标是在疾病早期阶段识别恶性病变，此时有效治疗方案更为多样，有助于改善患者预后。提升临床诊疗效能的需求持续推动着多项创新技术应用于实践，以优化患者管理，在此背景下人工智能发挥着关键作用。人工智能可贯穿肺癌筛查全流程：首先在筛查项目的低剂量计算机断层扫描采集中，基于人工智能的重建算法能在保持最佳图像质量的同时进一步降低辐射剂量；通过整合分析海量影像与临床数据进行风险分层，人工智能有助于实现筛查项目的个体化定制；计算机辅助检测系统能以高灵敏度自动识别潜在肺结节，作为同步或二次阅片工具显著缩短影像判读时间。结节检出后需进行良恶性鉴别，目前存在两种基于人工智能的解决方案：一是通过自动分割技术评估病灶尺寸、体积及密度特征；二是先进行分割处理，再提取影像组学特征以全面解析异常区域，实现所谓"虚拟活检"。本综述旨在系统阐述人工智能在肺癌筛查领域的所有潜在应用。
Artificial Intelligence in Lung Cancer Screening: The Future Is Now
肿瘤(癌症)患者之家