介入肿瘤学是一个快速发展的领域，在肝细胞癌 (HCC) 的微创图像引导局部区域治疗方面取得了进展，包括经动脉化疗栓塞、经动脉放射栓塞和热消融。然而，目前的 HCC 标准化临床分期系统在优化患者治疗选择方面的能力有限，因为它们主要依赖于血清标志物和放射科医生定义的成像特征。鉴于治疗反应的变化，需要更新的评分系统，包括疾病的多维方面，包括定量成像特征、血清标志物和功能生物标志物，以对患者进行最佳分类。凭借大量的数字医疗记录数据和成像特征，研究人员转向基于图像的方法，例如放射组学和人工智能（AI），以自动从图像中提取和处理多维数据。这些数据的综合可以提供临床相关结果，以指导个性化治疗计划并优化资源利用。机器学习 (ML) 是人工智能的一个分支，其中模型从训练数据中学习并通过自学做出有效的预测。这篇综述文章概述了 ML 的基础知识，并全面概述了其在预测 HCC 患者接受微创图像引导治疗后的治疗反应方面的潜在价值。© RSNA，2023。

Interventional oncology is a rapidly growing field with advances in minimally invasive image-guided local-regional treatments for hepatocellular carcinoma (HCC), including transarterial chemoembolization, transarterial radioembolization, and thermal ablation. However, current standardized clinical staging systems for HCC are limited in their ability to optimize patient selection for treatment as they rely primarily on serum markers and radiologist-defined imaging features. Given the variation in treatment responses, an updated scoring system that includes multidimensional aspects of the disease, including quantitative imaging features, serum markers, and functional biomarkers, is needed to optimally triage patients. With the vast amounts of numerical medical record data and imaging features, researchers have turned to image-based methods, such as radiomics and artificial intelligence (AI), to automatically extract and process multidimensional data from images. The synthesis of these data can provide clinically relevant results to guide personalized treatment plans and optimize resource utilization. Machine learning (ML) is a branch of AI in which a model learns from training data and makes effective predictions by teaching itself. This review article outlines the basics of ML and provides a comprehensive overview of its potential value in the prediction of treatment response in patients with HCC after minimally invasive image-guided therapy.© RSNA, 2023.