Ji G W, Wang K, Xia Y X, Li X C, Wang X H
Hepatobiliary Center, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Zhonghua Wai Ke Za Zhi. 2020 Oct 1;58(10):749-753. doi: 10.3760/cma.j.cn112139-20200605-00439.
Radiomics, 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.
放射组学作为一种新兴的组学技术，通过从数字医学图像中提取无数定量特征来展示图像的病理生理信息。近年来，放射组学研究数量呈指数级增长。目前，放射组学在肝胆疾病中的应用包括：肝纤维化评估、良恶性肿瘤鉴别、生物学行为预测、治疗反应评估及预后判断。将放射组学分析与机器学习算法相结合，已成为一种用于预测肝癌肝纤维化分期、微血管侵犯及切除后复发、胆管癌淋巴结转移以及结直肠癌肝转移治疗反应的高性能非侵入性方法。尽管该技术在临床转化方面仍面临挑战，但在大样本量的多中心研究或多组学研究的支持下，放射组学在促进肝胆疾病精准诊断和治疗方面将具有广阔的应用前景。
