肝细胞癌（HCC）是一种生物异质性肿瘤，其特点是具有不同程度的侵袭性。目前 HCC 的治疗策略主要由总体肿瘤负荷决定，并且由于其异质性而没有解决 HCC 患者的不同预后问题。因此，利用影像数据预测 HCC 对于优化患者管理至关重要。尽管一些放射学特征已被证明可以指示 HCC 的生物学行为，但 HCC 预测的传统放射学方法是基于视觉评估的预后结果，并且受到主观性和观察者间差异的限制。因此，人工智能已成为基于图像的 HCC 预测的一种有前途的方法。与传统的放射图像分析不同，基于放射组学或深度学习的人工智能利用大量图像衍生的定量特征，有可能提供对肿瘤表型的客观、详细和全面的分析。人工智能，特别是放射组学，在各种应用中显示出潜力，包括预测微血管侵犯、局部治疗后的复发风险以及对全身治疗的反应。本综述强调了人工智能在 HCC 预测中的潜在价值及其局限性和未来前景。版权所有 © 2024 韩国放射学会。

Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.Copyright © 2024 The Korean Society of Radiology.