Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
Big Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea.
J Gastroenterol Hepatol. 2021 Mar;36(3):561-568. doi: 10.1111/jgh.15414.
Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.
最近，放射组学和深度学习作为计算机图像分析方法引起了关注。放射组学和深度学习可以使用高维图像衍生特征执行诊断或预测任务，并有潜力扩展肝脏成像的能力，超出传统视觉图像分析的范围。最近的研究表明，这些技术在肝脏成像的各个领域具有潜力，包括肝纤维化分期、恶性肝肿瘤预后、肝脏肿瘤的自动检测和特征描述、自动腹部器官分割和身体成分分析。然而，由于之前的大多数研究都是初步的，并且主要集中在技术可行性上，因此放射组学和深度学习在临床实践中的应用还需要进一步的临床验证。在这篇综述中，我们介绍了放射组学和深度学习的技术方面，并总结了这些技术在肝脏放射学中的应用的最新研究。
