1 Department of Radiology, New York University Langone Medical Center, 660 1st Ave, Rm 336, New York, NY 10016.
2 Population Health Department, NYU Langone Medical Center, New York, NY.
AJR Am J Roentgenol. 2019 Jan;212(1):26-37. doi: 10.2214/AJR.18.20218. Epub 2018 Oct 17.
Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas.
We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.
由于在广泛的放射学应用中取得了有希望的结果，机器学习最近受到了相当多的关注。在这里，我们回顾了最近在脑肿瘤成像中使用机器学习的工作，特别是脑胶质瘤的分割和 MRI 放射组学。
我们讨论了现有的资源、最先进的分割方法以及脑胶质瘤的机器学习放射组学。我们强调了这些技术的挑战以及它们在临床诊断、预后和决策制定方面的未来潜力。
