Institute of Computational Health Sciences, UCSF, San Francisco, California.
Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California.
J Am Coll Radiol. 2018 Mar;15(3 Pt B):543-549. doi: 10.1016/j.jacr.2017.12.006. Epub 2018 Feb 1.
Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.
企业影像已经将各种技术创新引入临床放射学领域,包括先进的成像设备和后获取迭代重建工具,以及图像分析和计算机辅助检测工具。最近,定量图像分析领域的进步,加上基于机器学习的数据分析、分类和集成,迎来了放射组学的时代,这一范式转变在临床决策支持以及药物发现方面具有巨大的潜力。然而,要将放射组学纳入临床应用系统和商业可行的解决方案,还需要考虑一些重要问题。在这两部分系列中,我们从方法学到临床实施(第 1 部分)和从临床实施到企业发展(第 2 部分),深入探讨了放射组学转化管道的开发。在这两部分系列的第 2 部分中,我们研究了策略管道的组成部分,从临床实施到构建企业解决方案。
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