Computational Radiomics System to Decode the Radiographic Phenotype.
Computational Radiomics System to Decode the Radiographic Phenotype.
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DOI：
10.1158/0008-5472.can-17-0339
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发表时间：
2017-11-01
期刊：
影响因子：
11.2
通讯作者：
Aerts HJWL
中科院分区：
文献类型：
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作者：
van Griethuysen JJM;Fedorov A;Parmar C;Hosny A;Aucoin N;Narayan V;Beets-Tan RGH;Fillion-Robin JC;Pieper S;Aerts HJWL
放射组学旨在通过使用自动算法量化医学成像的表型特征。放射组学人工智能（AI）技术，无论是基于工程硬编码算法还是深度学习方法，都可用于开发基于非侵入性成像的生物标志物。然而，缺乏标准化的算法定义和图像处理严重阻碍了结果的可重复性和可比性。为了解决这个问题，我们开发了PyRadiomics，这是一个灵活的开源平台，能够从医学图像中提取大量工程特征。PyRadiomics在Python中实现，可以独立使用或使用3D-Slicer。在这里，我们讨论了PyRadiomics的工作流程和架构，并展示了其在表征肺部病变中的应用。源代码、文档和示例可在www.radiomics.io上公开获取。通过这个平台，我们的目标是建立放射组学分析的参考标准，提供经过测试和维护的资源，并发展放射组学开发人员社区，以满足癌症研究的关键需求。
Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io. With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.