Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.
Korean J Radiol. 2020 Apr;21(4):387-401. doi: 10.3348/kjr.2019.0752.
Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.
近年来,影像组学和深度学习在各种肝脏疾病的影像学评估中受到关注。最近的研究表明,影像组学和深度学习在肝纤维化分期、门脉高压检测、局灶性肝脏病变特征分析、恶性肝脏肿瘤预后以及肝脏和肝脏肿瘤分割方面具有潜在的应用价值。在这篇综述中,我们概述了影像组学和深度学习的基本技术方面,并总结了这些技术在肝脏疾病中的应用的最新研究。