From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston.
Department of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX.
J Comput Assist Tomogr. 2021;45(6):805-811. doi: 10.1097/RCT.0000000000001183.
The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imaging, including the lack of availability of a large number of annotated data sets and lack of use of consistent methodology and terminology for reporting the findings observed on the staging and follow-up imaging studies that apply to a wide spectrum of solid tumors. This short review discusses some potential hurdles to the implementation of machine learning in oncologic imaging, opportunities for improvement, and potential solutions that can facilitate robust machine learning from the vast number of radiology reports and annotations generated by the dictating radiologists.
机器学习在临床放射学实践中的应用，特别是在肿瘤影像学实践中的应用正在稳步发展。然而，在肿瘤影像学中广泛应用机器学习存在几个潜在的障碍，包括缺乏大量注释数据集，以及缺乏用于报告适用于广泛实体瘤的分期和随访影像学研究中观察到的结果的一致方法和术语。这篇简短的综述讨论了在肿瘤影像学中实施机器学习的一些潜在障碍、改进的机会以及潜在的解决方案，可以促进从放射科医生生成的大量放射学报告和注释中进行强大的机器学习。
