Afridi Muhammad, Jain Abhi, Aboian Mariam, Payabvash Seyedmehdi
School of Osteopathic Medicine, Rowan University, Stratford, NJ.
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
Semin Ultrasound CT MR. 2022 Apr;43(2):153-169. doi: 10.1053/j.sult.2022.02.005. Epub 2022 Feb 11.
Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.
人工智能已成为一个热门研究领域，其目标是将其整合到临床决策过程中。越来越多的预测模型正在被应用，这些模型利用机器学习，其中包括被称为放射组学特征的定量、计算机提取的成像特征，以及深度学习系统。在脑肿瘤成像中尤其如此，人工智能已被用于特征描述、鉴别和预后判断。我们回顾了当前关于基于机器学习和深度学习的人工智能在神经肿瘤学中的潜在用途的文献，这些用途涉及脑肿瘤分子分类、鉴别和治疗反应。虽然有支持在神经肿瘤学中使用人工智能的有力证据，但在将其引入常规临床决策方案之前，仍需要在更大规模的多中心进行更多研究，并建立简化和标准化的图像处理工作流程。
