摘要影像组学作为一项新兴的组学技术，可将数字医学图像转化成海量的定量图像特征，从而达到深入挖掘成像组织病理生理学信息的目的。近年来，影像组学相关的研究数量呈指数增长，目前在肝胆疾病中的应用主要包括肝纤维化精准评估、肿瘤良恶性鉴别、生物学行为预测、临床疗效评价以及预后判断。影像组学分析联合机器学习算法已在无创、高效地预测肝纤维化程度、肝癌微血管侵犯与术后复发风险、胆道恶性肿瘤淋巴结转移及结直肠癌肝转移治疗效果方面崭露头角。尽管该技术在临床转化过程中仍面临诸多挑战，但随着大样本、多中心和多组学研究的深入开展，影像组学将在推动肝胆疾病精准诊疗方面拥有广阔的应用前景。

abstractsRadiomics, as an emerging technique of omics, shows the pathophysiological information of images via extracting innumerable quantitative features from digital medical images. In recent years, it has been an exponential increase in the number of radiomics studies. The applications of radiomics in hepatobiliary diseases at present include: assessment of liver fibrosis, discrimination of malignant from benign tumors, prediction of biological behavior, assessment of therapeutic response, and prognosis. Integrating radiomics analysis with machine learning algorithms has emerged as a non-invasive method for predicting liver fibrosis stages, microvascular invasion and post-resection recurrence in liver cancers, lymph node metastasis in biliary tract cancers as well as treatment response in colorectal liver metastasis, with high performance. Although the challenges remain in the clinical transformation of this technique, radiomics will have a broad application prospect in promoting the precision diagnosis and treatment of hepatobiliary diseases, backed by multi-center study with large sample size or multi-omics study.