Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Department of Neuroradiology, University of Heidelberg, Im Neuenheimer Feld, Heidelberg, Germany.
Korean J Radiol. 2020 Oct;21(10):1126-1137. doi: 10.3348/kjr.2019.0847. Epub 2020 Jul 27.
Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-oncology. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. However, none of the potential neuro-oncological applications for radiomics and deep learning has yet been realized in clinical practice. In this review, we summarize the current applications of radiomics and deep learning in neuro-oncology and discuss challenges in relation to evidence-based medicine and reporting guidelines, as well as potential applications in clinical workflows and routine clinical practice.
影像学在脑肿瘤的管理中起着关键作用，包括诊断、预后和治疗反应评估。放射组学和深度学习方法以及各种先进的生理影像学参数，为神经肿瘤学中的放射学评估提供了很大的帮助。新技术的不断发展需要在临床试验中得到验证，并纳入临床工作流程。然而，放射组学和深度学习在神经肿瘤学中的潜在应用尚未在临床实践中实现。在这篇综述中，我们总结了放射组学和深度学习在神经肿瘤学中的当前应用，并讨论了与循证医学和报告指南相关的挑战，以及在临床工作流程和常规临床实践中的潜在应用。
