Park Ji Eun
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Brain Tumor Res Treat. 2022 Apr;10(2):69-75. doi: 10.14791/btrt.2021.0031.
The artificial intelligence (AI) techniques, both deep learning end-to-end approaches and radiomics with machine learning, have been developed for various imaging-based tasks in neuro-oncology. In this brief review, use cases of AI in neuro-oncologic imaging are summarized: image quality improvement, metastasis detection, radiogenomics, and treatment response monitoring. We then give a brief overview of generative adversarial network and potential utility of synthetic images for various deep learning algorithms of imaging-based tasks and image translation tasks as becoming new data input. Lastly, we highlight the importance of cohorts and clinical trial as a true validation for clinical utility of AI in neuro-oncologic imaging.
人工智能(AI)技术,包括深度学习端到端方法和结合机器学习的放射组学,已被开发用于神经肿瘤学中各种基于成像的任务。在这篇简短的综述中,总结了AI在神经肿瘤成像中的应用案例:图像质量改善、转移灶检测、放射基因组学和治疗反应监测。然后,我们简要概述了生成对抗网络以及合成图像对于基于成像任务和图像翻译任务的各种深度学习算法作为新数据输入的潜在效用。最后,我们强调了队列研究和临床试验对于AI在神经肿瘤成像中临床效用的真正验证的重要性。