Machine and deep learning methods for radiomics.
Machine and deep learning methods for radiomics.
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DOI：
10.1002/mp.13678
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发表时间：
2020-06
期刊：
影响因子：
3.8
通讯作者：
El Naqa I
中科院分区：
文献类型：
--
作者：
Avanzo M;Wei L;Stancanello J;Vallières M;Rao A;Morin O;Mattonen SA;El Naqa I
放射组学是定量图像分析的一个新兴领域，旨在将大规模提取的成像信息与临床和生物学终点联系起来。定量成像方法和机器学习的发展使数据科学研究有机会转化为更个性化的癌症治疗。越来越多的证据确实证明，非侵入性先进成像分析（即放射组学）可以揭示治疗过程中和治疗结束后多个时间点多个三维病变的肿瘤表型的关键组成部分。 CT、PET、US 和 MR 成像的使用进展可以增强患者分层和预后，支持新兴的靶向治疗方法。近年来，深度学习架构展示了其在图像分割、重建、识别和分类方面的巨大潜力。目前有许多强大的开源和商业平台可用于放射组学的新研究领域。然而，定量成像研究很复杂，应遵循关键的统计原理才能充分发挥其潜力。尤其是放射组学领域，需要重新关注最佳研究设计/报告实践以及图像采集、特征计算和严格统计分析的标准化，以推动该领域的发展。在本文中，机器和深度学习作为基于放射组学的特征或分类器的高级模型构建的主要计算工具的作用，以及放射组学的不同临床应用、工作原理、研究机会和可用的计算平台将通过主要来自肿瘤学的示例进行回顾。我们还解决与医学物理学中常见应用相关的问题，例如标准化、特征提取、模型构建和验证。
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that non-invasive advanced imaging analytics, i.e., radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, require a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
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DOI：
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发表时间：
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期刊：
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
影响因子：
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