Lung cancer is the leading cause of cancer-related deaths worldwide. Medical imaging technologies such as computed tomography (CT) and positron emission tomography (PET) are routinely used for non-invasive lung cancer diagnosis. In clinical practice, physicians investigate the characteristics of tumors such as the size, shape and location from CT and PET images to make decisions. Recently, scientists have proposed various computational image features that can capture more information than that directly perceivable by human eyes, which promotes the rise of radiomics. Radiomics is a research field on the conversion of medical images into high-dimensional features with data-driven methods to help subsequent data mining for better clinical decision support. Radiomic analysis has four major steps: image preprocessing, tumor segmentation, feature extraction and clinical prediction. Machine learning, including the high-profile deep learning, facilitates the development and application of radiomic methods. Various radiomic methods have been proposed recently, such as the construction of radiomic signatures, tumor habitat analysis, cluster pattern characterization and end-to-end prediction of tumor properties. These methods have been applied in many studies aiming at lung cancer diagnosis, treatment and monitoring, shedding light on future non-invasive evaluations of the nodule malignancy, histological subtypes, genomic properties and treatment responses. In this review, we summarized and categorized the studies on the general workflow, methods for clinical prediction and clinical applications of machine learning in lung cancer radiomic studies, introduced some commonly-used software tools, and discussed the limitations of current methods and possible future directions.

肺癌是全世界癌症相关死亡的主要原因。计算机断层扫描 (CT) 和正电子发射断层扫描 (PET) 等医学成像技术通常用于非侵入性肺癌诊断。在临床实践中，医生通过 CT 和 PET 图像研究肿瘤的大小、形状和位置等特征以做出决策。最近，科学家们提出了各种计算图像特征，可以捕获比人眼直接感知更多的信息，这促进了放射组学的兴起。放射组学是一个研究领域，利用数据驱动的方法将医学图像转化为高维特征，以帮助后续的数据挖掘更好地支持临床决策。放射组学分析有四个主要步骤：图像预处理、肿瘤分割、特征提取和临床预测。机器学习，包括备受瞩目的深度学习，促进了放射组学方法的开发和应用。最近提出了各种放射组学方法，例如放射组学特征的构建、肿瘤栖息地分析、簇模式表征和肿瘤特性的端到端预测。这些方法已应用于许多针对肺癌诊断、治疗和监测的研究，为未来对结节恶性肿瘤、组织学亚型、基因组特性和治疗反应的非侵入性评估提供了启示。在这篇综述中，我们对机器学习在肺癌放射组学研究中的一般工作流程、临床预测方法和临床应用的研究进行了总结和分类，