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肝细胞癌中的深度学习:现状与未来展望。

Deep learning in hepatocellular carcinoma: Current status and future perspectives.

作者信息

Ahn Joseph C, Qureshi Touseef Ahmad, Singal Amit G, Li Debiao, Yang Ju-Dong

机构信息

Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55904, United States.

Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.

出版信息

World J Hepatol. 2021 Dec 27;13(12):2039-2051. doi: 10.4254/wjh.v13.i12.2039.

Abstract

Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.

摘要

肝细胞癌(HCC)是癌症发病率和死亡率的主要原因之一。尽管经过数十年的研究和新治疗方案的开发,但HCC患者的总体预后仍然很差。在HCC患者的风险预测、早期诊断、准确预后和个体化治疗方面仍存在未满足的需求领域。近年来,人工智能(AI)技术在医学研究中的应用呈爆炸式增长,HCC领域也不例外。在各种基于AI的机器学习算法中,深度学习算法被认为是处理和加工从常规临床变量到高分辨率医学图像等复杂多模态数据的先进技术。本文将对最近发表的将深度学习应用于HCC患者风险预测、诊断、预后和治疗规划的研究进行全面综述。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f98b/8727204/4ad7542f3833/WJH-13-2039-g001.jpg

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