From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P., D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn (M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of Engineering, Los Angeles, Calif (D.R.A., W.H.).
Radiology. 2023 Oct;309(1):e222904. doi: 10.1148/radiol.222904.
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.
低剂量胸部 CT 用于肺癌筛查的实施为通过早期发现和干预来推进肺癌治疗提供了一个关键机会。此外，美国每年都会偶然发现数百万个肺结节，从而增加了早期肺癌诊断的机会。然而，要充分利用这些机会，就必须能够准确地分析图像数据，以便对结节进行分类和对早期肺癌进行特征描述。本综述介绍了使用语义特征对胸部 CT 进行传统图像分析方法的概述，以及使用 CT 衍生的放射组学特征和深度学习架构的技术和应用方面的最新进展，这些技术和应用用于对肺结节和早期癌症进行特征描述。目前，在将这些决策辅助工具转化为临床实践方面面临着方法学上的挑战，以及成像参数、最优特征选择、模型选择的异构性、以及对用于训练和验证的标注图像数据集的需求等技术障碍，都将进行回顾，以期最终将这些具有潜在强大作用的决策辅助工具纳入常规临床实践。
