Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.
Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.
复制标题
高级图像分析在CT检测的不确定肺结节和早期肺癌定性中的作用扩大。
DOI：
10.1148/radiol.222904
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发表时间：
2023-10
期刊：
影响因子：
19.7
通讯作者：
A. Prosper;M. Kammer;Fabien Maldonado;Denise R. Aberle;William Hsu
中科院分区：
文献类型：
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作者：
A. Prosper;M. Kammer;Fabien Maldonado;Denise R. Aberle;William Hsu
实施低剂量胸部CT进行肺部筛查为通过早期检测和拦截推进肺癌护理提供了一个至关重要的机会。此外，在美国每年偶然发现数百万个肺结节，增加了早期肺癌诊断的机会。然而，实现这些机会的全部潜力取决于准确分析图像数据以用于结节分类和早期肺癌表征的能力。本文综述了胸部CT中使用语义表征的传统图像分析方法，以及使用CT衍生放射组学特征和深度学习架构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。目前面临的方法学挑战，在翻译这些决策辅助临床实践中，以及异构成像参数的技术障碍，最佳功能选择，模型的选择，以及需要良好的注释图像数据集的训练和验证的目的，将审查，以期最终纳入这些潜在的强大的决策辅助常规临床实践。
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.